{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 股票指数简介"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 常用库准备：numpy, pandas, matplotlib\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 解决中文问题\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']\n",
    "\n",
    "# 解决负号问题\n",
    "plt.rcParams['axes.unicode_minus'] = False  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 导入外部数据: 'data/国内主要股指的日收盘数据（2015-2019）.xlsx'\n",
    "data = pd.read_excel('data/国内主要股指的日收盘数据（2015-2019）.xlsx',\\\n",
    "                           sheet_name = 'Sheet1',header = 0, index_col = 0,parse_dates=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 使用 DataFrame 对象自带的画图函数\n",
    "data.plot(subplots = True, layout = (2,3), figsize = (15,10), fontsize = 13, grid = True, \\\n",
    "          title = '国内主要股指的日收盘数据（2015-2019）')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 投资组合理论"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 股票挑选"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据导入"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 导入外部数据: 'data/全部A股2017-2019投入资本回报率ROIC.xlsx'\n",
    "stocks = pd.read_excel('data/全部A股2017-2019投入资本回报率ROIC.xlsx',\\\n",
    "                       sheet_name = \"Sheet0\", header = 0, index_col = 0,skipfooter = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>证券名称</th>\n",
       "      <th>投入资本回报率ROIC\\n[报告期]2017年年报\\n[单位]%</th>\n",
       "      <th>投入资本回报率ROIC\\n[报告期]2018年年报\\n[单位]%</th>\n",
       "      <th>投入资本回报率ROIC\\n[报告期]2019年年报\\n[单位]%</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>证券代码</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>000001.SZ</th>\n",
       "      <td>平安银行</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>000002.SZ</th>\n",
       "      <td>万科A</td>\n",
       "      <td>18.637132</td>\n",
       "      <td>21.212465</td>\n",
       "      <td>18.367560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>000004.SZ</th>\n",
       "      <td>国农科技</td>\n",
       "      <td>5.702958</td>\n",
       "      <td>-14.088504</td>\n",
       "      <td>-0.702403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>000005.SZ</th>\n",
       "      <td>世纪星源</td>\n",
       "      <td>3.802078</td>\n",
       "      <td>9.320341</td>\n",
       "      <td>9.774974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>000006.SZ</th>\n",
       "      <td>深振业A</td>\n",
       "      <td>14.403652</td>\n",
       "      <td>16.479529</td>\n",
       "      <td>13.669313</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           证券名称  投入资本回报率ROIC\\n[报告期]2017年年报\\n[单位]%  \\\n",
       "证券代码                                                \n",
       "000001.SZ  平安银行                               NaN   \n",
       "000002.SZ   万科A                         18.637132   \n",
       "000004.SZ  国农科技                          5.702958   \n",
       "000005.SZ  世纪星源                          3.802078   \n",
       "000006.SZ  深振业A                         14.403652   \n",
       "\n",
       "           投入资本回报率ROIC\\n[报告期]2018年年报\\n[单位]%  投入资本回报率ROIC\\n[报告期]2019年年报\\n[单位]%  \n",
       "证券代码                                                                           \n",
       "000001.SZ                               NaN                               NaN  \n",
       "000002.SZ                         21.212465                         18.367560  \n",
       "000004.SZ                        -14.088504                         -0.702403  \n",
       "000005.SZ                          9.320341                          9.774974  \n",
       "000006.SZ                         16.479529                         13.669313  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stocks.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['000001.SZ', '000002.SZ', '000004.SZ', '000005.SZ', '000006.SZ',\n",
       "       '000007.SZ', '000008.SZ', '000009.SZ', '000010.SZ', '000011.SZ',\n",
       "       ...\n",
       "       '688599.SH', '688600.SH', '688788.SH', '688981.SH', '689009.SH',\n",
       "               nan,         nan,         nan,         nan,         nan],\n",
       "      dtype='object', name='证券代码', length=4081)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stocks.index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据清洗"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 缺失值删除"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "stocks = stocks.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>证券名称</th>\n",
       "      <th>投入资本回报率ROIC\\n[报告期]2017年年报\\n[单位]%</th>\n",
       "      <th>投入资本回报率ROIC\\n[报告期]2018年年报\\n[单位]%</th>\n",
       "      <th>投入资本回报率ROIC\\n[报告期]2019年年报\\n[单位]%</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>证券代码</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>000002.SZ</th>\n",
       "      <td>万科A</td>\n",
       "      <td>18.637132</td>\n",
       "      <td>21.212465</td>\n",
       "      <td>18.367560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>000004.SZ</th>\n",
       "      <td>国农科技</td>\n",
       "      <td>5.702958</td>\n",
       "      <td>-14.088504</td>\n",
       "      <td>-0.702403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>000005.SZ</th>\n",
       "      <td>世纪星源</td>\n",
       "      <td>3.802078</td>\n",
       "      <td>9.320341</td>\n",
       "      <td>9.774974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>000006.SZ</th>\n",
       "      <td>深振业A</td>\n",
       "      <td>14.403652</td>\n",
       "      <td>16.479529</td>\n",
       "      <td>13.669313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>000007.SZ</th>\n",
       "      <td>全新好</td>\n",
       "      <td>-0.240154</td>\n",
       "      <td>-69.360871</td>\n",
       "      <td>13.788291</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           证券名称  投入资本回报率ROIC\\n[报告期]2017年年报\\n[单位]%  \\\n",
       "证券代码                                                \n",
       "000002.SZ   万科A                         18.637132   \n",
       "000004.SZ  国农科技                          5.702958   \n",
       "000005.SZ  世纪星源                          3.802078   \n",
       "000006.SZ  深振业A                         14.403652   \n",
       "000007.SZ   全新好                         -0.240154   \n",
       "\n",
       "           投入资本回报率ROIC\\n[报告期]2018年年报\\n[单位]%  投入资本回报率ROIC\\n[报告期]2019年年报\\n[单位]%  \n",
       "证券代码                                                                           \n",
       "000002.SZ                         21.212465                         18.367560  \n",
       "000004.SZ                        -14.088504                         -0.702403  \n",
       "000005.SZ                          9.320341                          9.774974  \n",
       "000006.SZ                         16.479529                         13.669313  \n",
       "000007.SZ                        -69.360871                         13.788291  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stocks.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['000002.SZ', '000004.SZ', '000005.SZ', '000006.SZ', '000007.SZ',\n",
       "       '000008.SZ', '000009.SZ', '000010.SZ', '000011.SZ', '000012.SZ',\n",
       "       ...\n",
       "       '688588.SH', '688589.SH', '688595.SH', '688596.SH', '688598.SH',\n",
       "       '688599.SH', '688600.SH', '688788.SH', '688981.SH', '689009.SH'],\n",
       "      dtype='object', name='证券代码', length=3953)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stocks.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "stocks = stocks.rename(columns={'证券名称': '证券名称',\n",
    "                       '投入资本回报率ROIC\\n[报告期]2017年年报\\n[单位]%':'2017',\n",
    "                       '投入资本回报率ROIC\\n[报告期]2018年年报\\n[单位]%':'2018',\n",
    "                       '投入资本回报率ROIC\\n[报告期]2019年年报\\n[单位]%':'2019'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "stocks_ROIC = stocks.loc[:,['2017','2018','2019']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>2017</th>\n",
       "      <th>2018</th>\n",
       "      <th>2019</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>证券代码</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>000002.SZ</th>\n",
       "      <td>18.637132</td>\n",
       "      <td>21.212465</td>\n",
       "      <td>18.367560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>000004.SZ</th>\n",
       "      <td>5.702958</td>\n",
       "      <td>-14.088504</td>\n",
       "      <td>-0.702403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>000005.SZ</th>\n",
       "      <td>3.802078</td>\n",
       "      <td>9.320341</td>\n",
       "      <td>9.774974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>000006.SZ</th>\n",
       "      <td>14.403652</td>\n",
       "      <td>16.479529</td>\n",
       "      <td>13.669313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>000007.SZ</th>\n",
       "      <td>-0.240154</td>\n",
       "      <td>-69.360871</td>\n",
       "      <td>13.788291</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                2017       2018       2019\n",
       "证券代码                                      \n",
       "000002.SZ  18.637132  21.212465  18.367560\n",
       "000004.SZ   5.702958 -14.088504  -0.702403\n",
       "000005.SZ   3.802078   9.320341   9.774974\n",
       "000006.SZ  14.403652  16.479529  13.669313\n",
       "000007.SZ  -0.240154 -69.360871  13.788291"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stocks_ROIC.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['000004.SZ', '000007.SZ', '000010.SZ', '000011.SZ', '000017.SZ',\n",
       "       '000019.SZ', '000032.SZ', '000038.SZ', '000040.SZ', '000045.SZ',\n",
       "       ...\n",
       "       '688396.SH', '688488.SH', '688500.SH', '688520.SH', '688521.SH',\n",
       "       '688536.SH', '688561.SH', '688567.SH', '688788.SH', '689009.SH'],\n",
       "      dtype='object', name='证券代码', length=887)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stocks_ROIC[(stocks_ROIC['2017'] <0) \\\n",
    "                                           |(stocks_ROIC['2018']<0)  \\\n",
    "                                           |(stocks_ROIC['2019'] <0)].index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 删除收益为负"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "stocks_ROIC = stocks_ROIC.drop(stocks_ROIC[(stocks_ROIC['2017'] <0) \\\n",
    "                                           |(stocks_ROIC['2018']<0)  \\\n",
    "                                           |(stocks_ROIC['2019'] <0)].index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>2017</th>\n",
       "      <th>2018</th>\n",
       "      <th>2019</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>证券代码</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>000002.SZ</th>\n",
       "      <td>18.637132</td>\n",
       "      <td>21.212465</td>\n",
       "      <td>18.367560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>000005.SZ</th>\n",
       "      <td>3.802078</td>\n",
       "      <td>9.320341</td>\n",
       "      <td>9.774974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>000006.SZ</th>\n",
       "      <td>14.403652</td>\n",
       "      <td>16.479529</td>\n",
       "      <td>13.669313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>000008.SZ</th>\n",
       "      <td>14.228685</td>\n",
       "      <td>5.342312</td>\n",
       "      <td>6.654695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>000009.SZ</th>\n",
       "      <td>4.399693</td>\n",
       "      <td>6.415402</td>\n",
       "      <td>6.940908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>688596.SH</th>\n",
       "      <td>9.158150</td>\n",
       "      <td>14.543768</td>\n",
       "      <td>17.507578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>688598.SH</th>\n",
       "      <td>21.739436</td>\n",
       "      <td>29.432076</td>\n",
       "      <td>31.965067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>688599.SH</th>\n",
       "      <td>6.670234</td>\n",
       "      <td>5.376122</td>\n",
       "      <td>5.362168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>688600.SH</th>\n",
       "      <td>36.208935</td>\n",
       "      <td>32.596370</td>\n",
       "      <td>32.133816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>688981.SH</th>\n",
       "      <td>3.549285</td>\n",
       "      <td>0.349828</td>\n",
       "      <td>1.307921</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3066 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                2017       2018       2019\n",
       "证券代码                                      \n",
       "000002.SZ  18.637132  21.212465  18.367560\n",
       "000005.SZ   3.802078   9.320341   9.774974\n",
       "000006.SZ  14.403652  16.479529  13.669313\n",
       "000008.SZ  14.228685   5.342312   6.654695\n",
       "000009.SZ   4.399693   6.415402   6.940908\n",
       "...              ...        ...        ...\n",
       "688596.SH   9.158150  14.543768  17.507578\n",
       "688598.SH  21.739436  29.432076  31.965067\n",
       "688599.SH   6.670234   5.376122   5.362168\n",
       "688600.SH  36.208935  32.596370  32.133816\n",
       "688981.SH   3.549285   0.349828   1.307921\n",
       "\n",
       "[3066 rows x 3 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stocks_ROIC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "stocks_ROIC = stocks_ROIC/100 +1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "stocks_ROIC['3-year-cum'] = stocks_ROIC['2017']*stocks_ROIC['2018']*stocks_ROIC['2019']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>2017</th>\n",
       "      <th>2018</th>\n",
       "      <th>2019</th>\n",
       "      <th>3-year-cum</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>证券代码</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>000002.SZ</th>\n",
       "      <td>1.186371</td>\n",
       "      <td>1.212125</td>\n",
       "      <td>1.183676</td>\n",
       "      <td>1.702161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>000005.SZ</th>\n",
       "      <td>1.038021</td>\n",
       "      <td>1.093203</td>\n",
       "      <td>1.097750</td>\n",
       "      <td>1.245691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>000006.SZ</th>\n",
       "      <td>1.144037</td>\n",
       "      <td>1.164795</td>\n",
       "      <td>1.136693</td>\n",
       "      <td>1.514721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>000008.SZ</th>\n",
       "      <td>1.142287</td>\n",
       "      <td>1.053423</td>\n",
       "      <td>1.066547</td>\n",
       "      <td>1.283388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>000009.SZ</th>\n",
       "      <td>1.043997</td>\n",
       "      <td>1.064154</td>\n",
       "      <td>1.069409</td>\n",
       "      <td>1.188085</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               2017      2018      2019  3-year-cum\n",
       "证券代码                                               \n",
       "000002.SZ  1.186371  1.212125  1.183676    1.702161\n",
       "000005.SZ  1.038021  1.093203  1.097750    1.245691\n",
       "000006.SZ  1.144037  1.164795  1.136693    1.514721\n",
       "000008.SZ  1.142287  1.053423  1.066547    1.283388\n",
       "000009.SZ  1.043997  1.064154  1.069409    1.188085"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stocks_ROIC.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 按三年累计ROIC降序排列\n",
    "stocks_ROIC = stocks_ROIC.sort_values(by='3-year-cum', ascending= False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>2017</th>\n",
       "      <th>2018</th>\n",
       "      <th>2019</th>\n",
       "      <th>3-year-cum</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>证券代码</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>000631.SZ</th>\n",
       "      <td>1.828236</td>\n",
       "      <td>6.859614</td>\n",
       "      <td>5.918321</td>\n",
       "      <td>74.221634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>600519.SH</th>\n",
       "      <td>2.503968</td>\n",
       "      <td>3.131259</td>\n",
       "      <td>1.559972</td>\n",
       "      <td>12.231073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>600132.SH</th>\n",
       "      <td>1.463023</td>\n",
       "      <td>1.926574</td>\n",
       "      <td>3.081994</td>\n",
       "      <td>8.686976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>601098.SH</th>\n",
       "      <td>2.146223</td>\n",
       "      <td>1.731084</td>\n",
       "      <td>1.944482</td>\n",
       "      <td>7.224320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>603288.SH</th>\n",
       "      <td>1.627084</td>\n",
       "      <td>1.789931</td>\n",
       "      <td>2.253619</td>\n",
       "      <td>6.563371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>000858.SZ</th>\n",
       "      <td>1.643722</td>\n",
       "      <td>1.828693</td>\n",
       "      <td>2.126422</td>\n",
       "      <td>6.391734</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               2017      2018      2019  3-year-cum\n",
       "证券代码                                               \n",
       "000631.SZ  1.828236  6.859614  5.918321   74.221634\n",
       "600519.SH  2.503968  3.131259  1.559972   12.231073\n",
       "600132.SH  1.463023  1.926574  3.081994    8.686976\n",
       "601098.SH  2.146223  1.731084  1.944482    7.224320\n",
       "603288.SH  1.627084  1.789931  2.253619    6.563371\n",
       "000858.SZ  1.643722  1.828693  2.126422    6.391734"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stocks_ROIC.head(6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 投资组合选取股票数量\n",
    "nums = 6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['000631.SZ', '600519.SH', '600132.SH', '601098.SH', '603288.SH',\n",
       "       '000858.SZ'],\n",
       "      dtype='object', name='证券代码')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stocks_ROIC[:nums].index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "stocks_ROIC_index = list(stocks_ROIC[:nums].index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['000631.SZ', '600519.SH', '600132.SH', '601098.SH', '603288.SH', '000858.SZ']"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stocks_ROIC_index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>证券名称</th>\n",
       "      <th>2017</th>\n",
       "      <th>2018</th>\n",
       "      <th>2019</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>证券代码</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>000631.SZ</th>\n",
       "      <td>顺发恒业</td>\n",
       "      <td>82.823614</td>\n",
       "      <td>585.961429</td>\n",
       "      <td>491.832112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>600519.SH</th>\n",
       "      <td>贵州茅台</td>\n",
       "      <td>150.396776</td>\n",
       "      <td>213.125880</td>\n",
       "      <td>55.997223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>600132.SH</th>\n",
       "      <td>重庆啤酒</td>\n",
       "      <td>46.302294</td>\n",
       "      <td>92.657377</td>\n",
       "      <td>208.199428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>601098.SH</th>\n",
       "      <td>中南传媒</td>\n",
       "      <td>114.622265</td>\n",
       "      <td>73.108406</td>\n",
       "      <td>94.448242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>603288.SH</th>\n",
       "      <td>海天味业</td>\n",
       "      <td>62.708422</td>\n",
       "      <td>78.993140</td>\n",
       "      <td>125.361904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>000858.SZ</th>\n",
       "      <td>五粮液</td>\n",
       "      <td>64.372233</td>\n",
       "      <td>82.869294</td>\n",
       "      <td>112.642193</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           证券名称        2017        2018        2019\n",
       "证券代码                                               \n",
       "000631.SZ  顺发恒业   82.823614  585.961429  491.832112\n",
       "600519.SH  贵州茅台  150.396776  213.125880   55.997223\n",
       "600132.SH  重庆啤酒   46.302294   92.657377  208.199428\n",
       "601098.SH  中南传媒  114.622265   73.108406   94.448242\n",
       "603288.SH  海天味业   62.708422   78.993140  125.361904\n",
       "000858.SZ   五粮液   64.372233   82.869294  112.642193"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stocks.loc[stocks_ROIC_index,]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "names = list(stocks.loc[stocks_ROIC_index,]['证券名称'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['顺发恒业', '贵州茅台', '重庆啤酒', '中南传媒', '海天味业', '五粮液']"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 投资组合的主要变量"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 预期收益率"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.random.random(nums)  # 从均匀分布中随机抽取6个从0到1的随机数作为初始占比"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.01782241, 0.39271833, 0.70215624, 0.62430067, 0.06584676,\n",
       "       0.94009084])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "weights = x/np.sum(x)  # dataframe 的向量计算"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.00649757, 0.14317448, 0.25598717, 0.22760314, 0.02400595,\n",
       "       0.34273169])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(sum(weights),2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 为了后面计算方便，定义一个获得权重的函数\n",
    "def get_weights(nums):\n",
    "    x = np.random.random(nums)\n",
    "    weights = x/np.sum(x) \n",
    "    return weights"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 案例"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 导入外部数据\n",
    "data = pd.read_excel('data/构建投资组合的六只股票数据（2017-2019）.xlsx', \\\n",
    "                     sheet_name = \"Sheet1\", header = 0, index_col = 0, parse_dates=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>顺发恒业</th>\n",
       "      <th>重庆啤酒</th>\n",
       "      <th>中南传媒</th>\n",
       "      <th>贵州茅台</th>\n",
       "      <th>五粮液</th>\n",
       "      <th>海天味业</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>日期</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-01-03</th>\n",
       "      <td>4.86</td>\n",
       "      <td>18.30</td>\n",
       "      <td>16.76</td>\n",
       "      <td>334.56</td>\n",
       "      <td>34.66</td>\n",
       "      <td>29.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-04</th>\n",
       "      <td>4.87</td>\n",
       "      <td>18.46</td>\n",
       "      <td>16.78</td>\n",
       "      <td>351.91</td>\n",
       "      <td>35.90</td>\n",
       "      <td>29.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-05</th>\n",
       "      <td>4.89</td>\n",
       "      <td>18.68</td>\n",
       "      <td>16.63</td>\n",
       "      <td>346.74</td>\n",
       "      <td>35.93</td>\n",
       "      <td>29.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-06</th>\n",
       "      <td>4.79</td>\n",
       "      <td>18.34</td>\n",
       "      <td>16.31</td>\n",
       "      <td>350.76</td>\n",
       "      <td>36.10</td>\n",
       "      <td>29.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-09</th>\n",
       "      <td>4.85</td>\n",
       "      <td>18.50</td>\n",
       "      <td>16.39</td>\n",
       "      <td>348.51</td>\n",
       "      <td>36.45</td>\n",
       "      <td>30.14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            顺发恒业   重庆啤酒   中南传媒    贵州茅台    五粮液   海天味业\n",
       "日期                                                  \n",
       "2017-01-03  4.86  18.30  16.76  334.56  34.66  29.72\n",
       "2017-01-04  4.87  18.46  16.78  351.91  35.90  29.73\n",
       "2017-01-05  4.89  18.68  16.63  346.74  35.93  29.83\n",
       "2017-01-06  4.79  18.34  16.31  350.76  36.10  29.60\n",
       "2017-01-09  4.85  18.50  16.39  348.51  36.45  30.14"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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tVka/PAuVWskMX1oOYS6zoXd3DcW6Dgzn5O40MpOKrntxnOyQSY7JpXknifh9Zaj13bGZ9qPRlpKfUYZ3oBslBWa2fXeKssIEzAUryDr/FNDtuu57qeI8EzE7Uuk7rnWNreMaigiCqzh06BCLFy92BqE6nQ6VSuXsFrFx40aKioqYOnUqX375JZs3b2bFihWoVCpCQkJYvXo1kyZNYtq0aUycOJHw8PAa71NaWvk1x8iRI+natSugZI9/+eUXIiIinO8/9dRTWCyWSy8hCIIgCEIjk5deCjiQZZmA8ObEH9xH1PpVRI55BACNxjUTbDba0BtcO0n4hhhQa1TkXCimfe8Q5/HslGJ+/XcsI5/rSkCYJ8V5JvYsj+e+KR3RudUczhXnm5T72ZWOVGp9F2ymg3gHlFGYBx36NiWsnS9HNqeQmBEFgLH4Qn1+JMiyzOrPj1JWZKF9nxACwjzr9frXQwTBVURGRrJt27bLjqnIypaUlPDiiy86u0IAtGjRgp07d16xfGHHjh3O16GhoYSGhgJKVvhSixYtquv0BUEQBEFoQAUZpbh5grkAug4exoVTMRxctYzuQ0eiN3ig0amQHTIOuwOVWoW5zFat/latVhEQ5kH2hWLnMZvVztbvT1GYZST+cCYBYZ7sX3WOxOhsWvUIcgmWq8rPUEoqspOP0bRdR/zCWlGa24eU2P10HtSLTvcE4eHrSWhbP755vpgSM9jMxTVe61oV55ooyjHRb3zbRhUAg6gJvmoqlQqVSoW3t7dLAFz1fbVa7PIiCIIgCLcbU5kNN4Pydb9Gp6Pvw49jMRlZ+rdZbP7nFySf2IQsO8ozxlBWmEt20vpqtcOBzb3IuVDiTKodWJ1IfnopHj46kmKVtmpWs+vOc5ey2xxcOJWHw55PQUYK7fvcw8hpXXnglZfxDmpC7G9fsuC5CRxctYzSgnxK8pTr2q152O1KNru08Pq7VGQlK0F109Y+132t+iaCYEEQBEEQhHpgNdlQa5VvjDU6PcGt2jDs2RdxOBwkHT9KUvRGHNY4ln5wmF1L4yhI20zehf0kx0S7XCeomRfmMhvFuSZSz+Rx/LcLdL03jG6Dm5GbWsLmhTEU55oAKCuquWTy4JpEjv92AUlWSiHa9r4HAL3Bg9EvveEct+eX/3BwlbLfmVrrhuwoxGqyc2Z/Ootm7SUrucjlule7WD87pQiVWiIgtHFlgUEEwYIgCIIgCNelIjC0mOxoNEoQrNXrAaUs4qnPvua5rxfh7h2A3RIHQMzOVCriSZvZNeMa1MwLUHaU++2H0/gGG+j7cBtadA0A4NzRbHIvlgDUmq1NiskBwGGNp2mb9ngHBjnfC2nTjgdn/oWe948BIHrzOtr17U9I6x7IDiMWo43T+5RWrmcPZDjPyzhfyDcv/s72xadZ80W0S4C8778JrJt/rNo8spKLCQjzRK1tfCGnqAkWBEEQBEG4RkkxOWz8+gSP/603FpMdN0NlJrgqSaXCw7cJptICmnf2J3J0K377bg/pcWC7ZAF8QJgHkkpi99J4bFYHD79+J1qdmoBQTx6a2RM3Ty1nDmRwdHMyx7ddIHJ0y2qL4+w2B5IqH6spgw73PFBt3m3v6kubXn0ICG9OQFgzwjp0Yu3nX4FsxGy0kpuqBNk55f8FyLlQgt3m4PReJUCOO5xJkxbe2Kx2oremAGAqteLmoSz2k2WZ7JRiWt/Z5Ho+4hum8YXlt5Do6Gi2bNlS6/vFxZXF5UlJSeTn59+MaQmCIAjCbU12yMTsTMVisl158HVKOZWHLMOhdeeVcgi1Uqt7aRAMoHXzQnbkgZxHUAsPAsKUOllTietiNI1OjV+IAavZTq+RLQhu6e18r2kbX/xCPOg7tjV+IUoLteTYXJfzjcUWinJMhLZSMrVt+9xT49wlSaLbfcMJ69AJAHcvb8BOQWYRFpPyHMYSq3N8aYEJh/UC4167AwCLUfl8MxMrM8Lp5wqdr4tyjJjLbDRp7lXj/RuayATX4r333iM0NJSpU6c6jx07dowNGzZgMBiQJImDBw9isVg4deoUoHSOGDZsGF26KNsPPv3007z44ov079+fL774gscff5zevXu73Cc7O5v33nuPd999l9dff51JkyYRERHB0qVL8fX1ZcKECfj4NL5ickEQBEForPLSS9n1SxwOu0z3+5rdsPscWHOOmB2pAFw4nYfskJHUruUQVak1BpDNxO39nKSj/8RiNAKw44d/4R/eHHNpCTaLhc733kerHkEYvHXceX9ErfcfOa0rP88+iLHY6nK8opRBqytGb/DA0y+gTs9j8FGC7czEDGRHCe5eHphKKrPU549uxlKyCbvlDoJbehN/OJP89FL8q9T7JsXk0LJbIFC5KK5JC28aIxEE10Kv16PTubYtCQsL4/7770ev16NSqTh8+DATJ0507h5nt9ud7c6ys7OJi4sjMTGR2bNnc+rUKaKjo9FqtXh7e7Ny5UoAjh49SocOHTh37hypqans3buXM2fOcOrUKQwGA/369RNBsCAIgiBchYrOCRfO5N3QIPjIpmTna3OZkhVVqyoywdW3HvZrGkjqKfBt2gLvQD9SYipraDd/9TmlBco3xhHde9L7wVaXvbfVYsYrQI8kKZnfnNQSorckM2hSB2J3XSSsnS/G4mx8Q0KRpLptUOHhp+wolxRzEXPhv8Aeiko3QQnuVRKF2UkA5Ken4R0QTub5IjISlT/uXlrCO/hz7kgWkaNbotWryUouRqWR8A/1qNP9bzYRBFfRsWNHwsLCAEhJSUGn0/Gf//wHk8mEp6cnixYt4pVXXnGOP3nyJOfPn3cGyxqNxtlneO7cufTo0YNJkyYxZcoUhg4dytatWwGwlbdCWbFiBW+99RZt2rRh06ZN3HHHHc4d4zIyMpg3b54zwBYEQRAEoW5sFiUQvRhXgN3mQK258dWfkkpCdsigUn7Ha2rIBAeE+QHQvHNHwjt2cQmCSwsLnK/zLl7Aw9evxvvkpqbw++JvSY45joefH1r9gxiLLaz/+zFKCy34hRgozjVx90Nt2PF9Gk3btq/zM/iHBpTf/yIA5pI03PyUTT0sJhulBUXOOfg0aQeA3qDB09+NgFAPeg5vwfnj2Syatdd5zdC2vjfl878WjTII/uTQJ5zJq9+9qzv4d2BW5KzLjtFqtc4gdt68eYSEhPDkk0+SlJTEjBkznON27tzJRx99xLp16ygrK+Onn37i1VdfdZY6nD59mi1bttCjRw/69euHXq/n5MmTDBkyBFDKJmbMmMEjjzzCihUr+Oqrr3jrrbewWq3MmjWLpk2bsmHDBrFdsiAIgiBcpXNHs8hKUb6Gt5ntZJ4vJLRtzQHl9ZAdrq3CQlp5k55QiEqqPRNsNSnlD+5ePhi8fZ3HJ7z7KeeOHiJ2x1aMRYVkJiYQtWE1eakX6Hn/g3QZOBStmxuZiQn8+Naf0Bs86HrfcOIO7MFh3EFBZgtKC5WyhYNrz2Pw0RHazkBRThZdBg6p2/PIMm5WpawiMDSZ9PiK41ZMJVZO709HtiuBes6FJCLvUr6ltlsdTHg70pktHjy5I1v+fdJ53YiugXW6f0NolEFwQ6lp84uqKrZTvnDhAlqtlrVr17J161Y6d+5MamoqWq2yGvLo0aN89NFHLF26lH379vHrr78SHR3Nm2++6XK94uJidDodcXFxdOvWjT59+nDmzBmef/55XnvtNe64444b86CCIAiC8D9q88JYl58vnM6/IUFw1QVgbXs1ISDck/SEQkDJBGt1btXO6TZ0JNkpSdw5eiwluTnO42EdOhHWoRP9H5/C36c8yu8/fockqQhsEcH2779h34ol3DF8FCd//w2AO0ePpe/Dj2M1GTl7IIqLcQUu9wkM9yInORFkmZA27Wp9BltODrbsbNw6diT9zbcoWLOGtiMGER9fGcTaTdEUZvfCXGoFWelNnJ2cREgrpc43pHwTDEkllX8WwSBDemIhOGQ63t20zp/pzdYog+ArZWxvFJvN5szWVpRDLFq0CJPJhLd3ZVF3dnY2BoOBzZs3A+Dh4UF6errz/SeeeIKkpCTnz6mpqWzevJnNmzdz5swZPv30UyZPnkxmZia5ubksXboUlUrFqlWrSE5OJjk5mfnz5/PDDz8wefJkJk2adHM+AEEQBEG4hVWUQVQw+Oi4cDrvivW11+LIpiTcvbQ8+tZdGLx1FGYbObTuPBq9kiGuqRzC4O3DA68oCTGHrXrnCkmSuOexJ9m/YgnDp71Mm8i+XDx7isNr/8v+FUsAaNenH5FjHgXAN6QpdksRWk8bI5+7g91L4yjJN+PmoSEtTvnHQHCrNrU+Q8of/og5Pp6AqU9TuGYNEtDpbBKh05/hwskTFOfmkZeexOaFMVjNJsCOp58/Jfl52MwlPPpWL7wD3atdt+1dwbS9K/hqPs4G0SiD4Iby3XffERkZCbiWQ5hMJuLi4pzj5s+fT0pKChkZSgPp9PR0zp49W+t1n376ae677z7CwsK45557mDBhAgBt2rTh66+/pkOHDuzfvx8PDw/efvtt/P39adOmDYsXLxZbMAuCIAhCHWWlZGEzH0et64YkSbTsFsipPWnkZ5TiF1J/i7OykotIOZVHn7Gt8PJXMr7+TT14+rP+bP/uH6g1GtSay4dYSjuy6u4cNZaeIx9EKv92OrxDZ8I7dCY39QLnjhykx/BRzmv7hiiL8e+bHEarHkFEb0mmJN+MzqAmdudWwjp0wuBd8+J6W04O5nil5iH3u+/x6N8ftbc3pQcO0Gv0OHqNHse6Lz7BYkzAv5kXafFKtjm8U1fO7P2d7JQkIrrd2t9YN85K5QZSEQCDUrdbsQOMm5sb3bp1w1LezHrRokVs376dV199lVdffZVt27bx5ZdfulxLlmXn+QUFBUydOpWBAwcydOhQ50I6i8XCm2++yffff8+SJUuYMmUKn3zyCQEBAYwdO5b+/fuzf//+m/HogiAIgnDL2/XjQmxlvyHbMwGI6BaILMPPsw/Wur3w1XLYHRxYfQ69QUPXe8Nd3jOXFnBq13a6DRl5xY4MqvIkV3inLtXek2oozwwIb0bkmEfQuVVmXv3Kg2CVqoCyokJMRceQZZmy/DgKMzO4Y0T1TTIqWJKVzhbBb71J2927aP6vhWhDm2IvKnLGLx6+vhiLC7lrdEtkh1IK0axTVwByUpIu+3y3ApEJrkVJSYkz6K1gLO/nV6Fly5bOxWuvvPIKTZs2dRlb0QXC19eXbdu2sWbNGmbNmsWQIUMYNGgQq1atYtSoUUyYMIGsrCwWLFhAREQEpaWljB8/nnbt2hESEnKDn1QQBEEQ/jeU5GYD4LBnotKEEN6hshY47lAGXQeGX3engi3fnuTC6XwGTGiHzl0Jo7Ys/Du+wU2RVCpk2cEdI2sPPqt6/l8/uQS1VyuweQRqjYb0+LMkHNpPRvxOdF6PkZFwAk//ANrc1bfWc63pyrfZHvfcgyZA6Qqh8vYGqxXZaEQyGPDw8cNiLCOouRtyeT2wX2gYBh9fclKSa732rUIEwbV47733qh1r0aIFO3fudP583333OV9//vnnLv/q69SpEz/99JPL+WPGjHHJBD/22GPO91566SXn6127dgHQo0eP63sIQRAEQbiNWExlADhs6UjuPdBo1Yx4tgubF8ayd0UCp/elM3hyR4Ijrn3zhtSz+TTv5E+Xe8Ocx+IP7MVmsRDQrAUB4c2dGdorqa1UoS5Kfv+dC89NI+iB+zgfHYW2fPG+zRRFXvE57nls0mVLMmyZShCsqZJsU5fPx15UhMpgwOCjdLA4u+93QlslknwC3Dy9CGweQeqZWM5HR7F/5S/cMXw00ZvWMfz5PxEQ7tqX2W6zsWbu+2Sci2fSp/Px8m883SJEOUQ9qWsjaoPBgOYKdUKCIAiCIFwdq8mEsUjJBMuOImcLs6AqW/Yaiy1s+ucJ59f9V0t2yMo2wBHezt/7VosZU2kJNquFzMR4mkTU/yK8mhSuXQdA+7AI8tJSyUhQ1i45rOeQVBq63Te8hvk7yPx0LgX/XYk1IxOVpydqz8rd3tTlO8bZC5V+wBW9irf9+yuSTxwFwN3Ti479BlKYmcHKj2eTHneGjX+fR3rCWWJ3bq12zx2ikhQAACAASURBVJjtWzh/7AjBrdrU2DGjIYkgWBAEQRCEW17OhWRABtTgKHYed/eq7Nfbc7jST/fSbYbrymy0gQxuHlrnsdJ8ZZc3vYey8C6wecQ1XftqyXalE0bbth154sPPaVFlkVpgs7bOLC6AbLOR95/FZH7wAXnffUf6X/5C/uLFaEJcOzioyzthOYqU9m9NWrYmKKIVkWOVbhRqjQaDry+d772PLoOGOs/r9cBDQPU6YYvJyP4VPxPesQsPvfUublUC7sZApCQFQRAEQbjlZSUlAqDStsRhPe/M9mr1lV2WAsKVICwntZjmnQKqXSM/oxS1RlVj2y8AU6kSPLt5VIZPJfm5ANwz/kkOr1tJi643p5RRtpUH8jYbwa3a8Mhf3mfNvA9JOLwf/7Agl7GFq1eTOWcOAGo/P3wfeZjc774n4A9/cBmnqlIOAeDp58/kT+YD0LL7nfiFhqFSKZ/n0GdnUFZYQOeBQ2jX+x6sJhOn9+zA4bCjUqkxlZZwZMNqygoLGPv6X+v8jfnNJILgRkaW5Ub5F0UQBEEQGrPs5PNIkh7voFYUpCU4N3aoKiBUCYLXzT/O818NJP5wJlaznS73hiPLMj/PPgjA9AWDa7xHRRCsd8kE5wFK14TLdWOod1Zl8b29uMR5yM1TKf0wePsomd+5c2l/6CA5X//TOcb9zp40ee01AqdPR+XmWp6gCVLqdY3HjuF5770Yjx/HcOedQPUuFiqVmnGz/ub8OaxjZ45v3cj/PT4G35CmFGQo+ye0633PVW3dfDOJIPgS+fn5HD9+HIBNmzZht9sZPXo0AG3btiUsTCmE//TTT+nWrRsjRoxwnmu32+nfvz/79u0jNTWVtLQ0MjMz+eCDD/jqq68wmUz88Y9/5Oeff0aWZcLCwggNrSye37dvHx9++CHr168XgbAgCIIgXIWspEQkdSBNIppSkAayXBkcDnmqI+5eOgzeOkLb+pIWX0BmUjE7fjyLV4AbXe4NJy+t1Dm+rMiCwbv6tsdJx5Vd3qqWQxTnKHXInjd5wZdc3oHKUVJZ+qErD2rdvb3Jmv0RWK1kzJ6NNS2NZv9aiMNkwtCrF0C1ABhAGxyMz5gHyV30A2VHjmI8epSWa1bj1v7KQWx4x84AuHl4EtSiJe379qessIA+D0/AeOIE2qZN0QQFXeEqN1edaoIlSfKXJGmoJEmNZ0nfDRIdHc13332HzWZDpVKhUqmw2Wzs2rWL9evXO8dJkoS+fDeYQYMGAaBWq3F3V75CSUlJ4cCBA8TExFBSUkJUVBQnTpzAaDQSFRXFoUOHXHaZKygo4KWXXmLOnDlIksQrr7zCmTNnbuKTC4IgCELjE7V+FQtf+MNlF7Plpl4gPf4MkjoIT39lMZdkX01hltIBoX2fpjTvrJQ/dL9P6V6w5dtY7DaHM7t7/ni283qZ5wu5lM1i58hmpS1Y1SC4MDsTvcHjuutdZbuds3f2Iu+HH+o03l4e/GZ/8SXmxPPKNagoAXFDXd72rHDNWtx79MCjXz+8hw5F43f5LaSbvPEGAMajykI4cx1jES//QCZ9Mp/nvlnMg6/+mX4TJjPsuZfw0LmRNP4x4vsPwJaTc+UL3URXDIIlSfID1gORwA5JkqqF8ZIkaSRJSpEkaWf5n643YK43hbe3N0lJSXzwwQesXr2atWvX8sEHH/Dbb7/RpEkT5zhJkpzZWg8PD5fjAHfffTeDBg1i8+bNmM1m1q9fz4YNGzCZTKxYsYJHHnmEO8u/YsjPz+fhhx/m448/pnv37oCyy9z48eNdtl8WBEEQhNvN74u/pTg321l2UJP9K34GlHpgT39lcZe5tJiz+/dUG+vhoySwSvLMuHtpMZdakR0y54/n4BWgZEdL8s3VzivIKnO+dq+SJS7MysS7yfVvEWwvKsJRWkrmRx9fcWzJ779jjk9w/pxR3ta1oiMGZjP23Fzn+4EvzqjzN8yagADCPv0Et85KZtdUZcfcK2kS0QqNVutyrGTHDgAknQ6usSvHjVKXcohuwKuyLB8oD4h7Ar/WMGaJLMuz6mNSGXPmYD5dv1lQfccOhPz5z5cds23bNo4cOcKDDz4IKNlcWZZp0aIFAHFxcaxdu5aFCxcycOBA1q9fT1F58fg777zD66+/7nK9rl27smvXLrZs2UJubi49evTgjTfe4JlnnnEJqEeNGkVSUhIfffQRn3/+uTMD3bJlS+6//352795NQED1An5BEARBuF2knjlJh7sH1PheQWYGwa26UJjfEu+Ayh7AaXHVYwkP38oAtvt9zTiwOpH//GUfJflmeo9pRdSGJIrzqtcT56crQfDwZ7qgd68Mn/LTLtZLRwhHYWX2OfGBB2n+wyI0/v7VxhWu30DazJno27XDXB6gGo8dw3T2rDPQLfjxRzyB5j/8gKTVYOjZ86rm4j1yJN4jR5I4ZiyWhHPX/lBA0a9b0ISE0Gb7bzXuhNeQrhgEy7L8O4AkSQNQssHVd5GAPsBoSZIGATHAc7Is2+pzojfDkCFDuOuuu3j44Yddjp8+fRqAb775hoiICObPn48sy0RGRrJ8+XIAevXqxcKFC53n7N+/nz//+c+o1WocDge7d+9GkiS6du3K/PnzmT9/Ph9++CG9e/fm22+/xc/Pj+Dg4Gr/Utu7dy++vr4IgiAIwu1I527AYixjw5ef0rLHnegNHi7vr/u/j8lMjKd51wGQDz5Blb8z0+PPVFtwXjWL6+nnmvlt1T2IM/vSKSkPgi+ezWf1/0Uzec7d5GeUIkkQ0a0yKXV86yYKMtNp2/vu635Oe5Ug2BwfT9HmzfhPnOgyxpaXR+nu3aiDAolYvoyz3ZVOFGofH86PGUtQUCARzZoQkpJMs38txKN35HXNSe3n5+wUAWA+f56M2e8SOudDtGGVm4XYi4q4OHMmfhMex2vwoMrjJaWU7tmD72OPNboAGOq4ME5S/vY8BuQDNTXXOwwMkWU5XZKk/wD3A2svucazwLMAzZs3v+z9rpSxvZEkSUKj0bB582bWrl1Lhw4daNeuHU899RRms5mkpCR69uxJZmYmI0eOxOFw8OOPPzJq1ChnQAzQu3dvtm3bhlqtZunSpXh5eZGWlsbhw4eRZZk+ffrQtatSNZKdnc2AAQOcGecKFy5cYPHixajVagRBEAThdmO3WbGYjM6fM87F06JrD1JPx3LuyCH6Pz6FuANKyYOkVoJjn+DKILissICi7Ex8mlTZFU2tBGPtege71Pa26BqAX1MDnv5uzkzw6X3K2p3D689jMdnxCnRHo638nZwcE41KreauB12TZ9f0rIWudcile/Y6g2DZbifr07lKvbBGg3uP7qj0esK//hqQ0bdpw7lhw9Fk59ApOwevkSPw6NPnuuek8vTAmldZhmI6cYKygwdJe/ttPHr3wXvkCGS7A1tGOqW7dlO6azcdTp9y/qOjdNfvyBYL3sOHXfdcboQ6heWyYjpwAniwhiEnZFmuWOUVBbSt4RoLZVnuJctyr6BGtjqwKqvViru7O3a7nc8++4ysrCzOnj1L165dcXNzo3Xr1nz66aecOnWK9u3bM2HCBEBZFFfxGkClUmG1Wnn77bfZsmULy5cvdwazkiSxf/9+DAYDADqdjjFjxhAVFeXy5/HHH0d7SW2NIAiCIPwvK8rOwmaxAFCQkQGyzMDJzwBKEAyw7N0/E7VuJcknoquc6YZGp8LNQ+9yvbT4s9Xu8cLXgxgypZNLENx3bGskScI70I3CbCXwrugxfHpfOueOZuEfovze/v3H71jx4V9JT4ijVc9I3L2ufRtmUNqjVgTB4Qv+iffo0ZTu24fDZMJhNJL68suVC+ZsNnTlWVivwYPwGjwYXfPmtDt00Hk9bdO6bdt8JWoPTxwllV027EXKYryy/QfI/uILzg0fQeKoURTv3OkcY6mylqlk717Uvr6431G5kUdjUpeFcbMkSZpc/qMvUFDDsMWSJHWXJEkNjAWO1+Mcbxq73U5paSlTpkxh7NixjB8/Hi8vL2bPns2vv/5KQoJShB4XF4e/v78zQK1pxermzZuJjIykZcuWLFiwgJSUFCzl/1ODEiRXUKvVrFmzhl69ern8WbJkiWiVJgiCINw2LMYyFr32Asd+VboxZZ5Xfu+26Nod/7BmnDuiBHoVX61v/ucXuHl6cf+M1/AKvAuDj97l96ZW78aJrZtYPfd9rKbKOl9JJSGpJDz9KgNmT3+lNMI32ICx2Iqp1IpG7/pNrF+Ikm1OPR1L8oloSnJzrrse2HzuHHF9+lK4RvkC3b17d3zGjEE2mSjasIHkyVMo+W07wW+9CeXPVrUUoYLa2xu1j7LZhbZp0+uaUwWVhweO0srWcRXt2PQdOlQOkmXyf/rZ+aMx+pjztS0rG21YGFIj/Ua7LpnghcAkSZJ2AWogVZKkDy4Z8x6wGDgG7JdleVv9TvPmKC4uZtasWcTHx/PVV18xffp0unfvzpIlS1iwYAFRUVEAvPLKK/zlL39xnte0/C+b3W6nrEwpnO/Zsydbt27l6aefRpZlZs6cyYwZM2q8r9VqrTUTXDVwFgRBEIT/ZWlnT2M1myjKVdqVZZ6LR6PX4x/WjO5DR5Ied4a0uDPOdmRlhQUMmTqdjv0HYSy24XFJb9+Q1m2V0omog6ye+x4FmRku73v46tFolVCoYrFbRaBbkFmGbHdNcvmWZ4KtJhNhHTpxx4gH6Dyg5o016qpk924chYWU7lHKOtReXhh6R6IyGEj/y9uYExII/+of+E+Z4uyuoGvZqsZr6Vq3Vq7hf/k2aHWl8vTEXlrqTPbZi4qR3N1p+d8VNJn5WuVAux21vz8qHx+M0Uex5efjKCvDlpuDOrDxLuyvy8K4fGDoJYffvmRMLEqHiFuar68vS5YsqfG9Vq1a8efyWuXly5c7SxkA/v3vfwNKRnffvn0ALt0fdDodq1evrvW+vXv3drZLq+qLL764+ocQBEEQhFtU6pmTAJiKlYxjRmICTSJao1Kr6TJoKPuW/cSRjWvQ6JRgt2P/QbTv2w9QNrjwD3VdNBfWsTOpZ04iOxykxJ7g25efoefIBxk05Rlkh4PoX9dz1ygtzbv0c57jF6z8fs9LK8Vscl3j799Uub7NYsYnqDWD//DcdT1vyd69ZH38CQDaFs1xFBUjaTRIgNfw4ZTs2U2zr/+Je1fX3do8+t1T4/UCpz3HhWefw61Tp+uaVwWVhwfYbMgWC5Jej724CLWXF5Jaja5V68pxBgPapk1RBwZQuG49Bf9dqSyqKyjArUPHepnLjSB2jLsGVQPg+qBWq8XiN0EQBOG2l3o6FgBTSTEOh52spHN0HawsqtK5udNtyAii1q1Clh106j+I4S/8yXluaaGF8A5KS7GwDp0wl5YS+eAjdLjnXozFRZiKi4nZ/ivHt27k7kcnsu7/Pib5RDTeQU3oOWKk8zo+TdwxeOtIPZOH/ZJMsF9FJthsRqN3rT2uqmjjRtBq8br3XvIWLyb/l6U0ff99PPr0dhlXsRGFJiSEiJ9+wppemalu+u5skCSkKmuDQt59F3NCQq0bXngOGECHUyfrrRODylMJ+h2lpaj0ehxFxai8la2ZtSGVvZFD536KpNFgy8vHci4RXauWlO7arTxbQPU2b42FCIIFQRAEQWhwVouZjITyvrclxeRdTMVmNhPSqnKt/R0jHuDw2v8CENg8ApVKXX6uHYvRhsFHyRBPePdT5zkBYc2cr0vyckk8epio9atIPhFNSOu2ZCQmUJyXQ2biOVJPxRDWvhPNuwSQeDQLrwA3Qlr5kJGoLFrTG5SA1Go2o9VX33YYQHY4uDjrTbC6NtNKefppgmfNwn/ypMqDkhKstvhxMZrAQDSBlRvzSrrq2zb7PTb+ch9h+SXrrxWZqnwzMEdJCfj74ygpRl2+CFBTpe7Y6777nK99x43FYTZXtm9rxPscNL6mbYIgCIIg3HYy4s9it9lw8/QiIyGOIxuUMsLgKkGwV0Ag3YaMwNPPn9a9KluAJZ1QtuNt0sLrsvfwLi9VTDh8ADdPL3oMHw2yzMLnn2LN3Pc5smE1W/71D7oNDMZuk8m9WIrOXcMjs3px/wtK1acsy9jMZrS1ZIIdRUVgteI1YoTzmMc99+DZvz+Zc+ZgSU6uHGtU1hFpQ0KqXacxUJfXXpfs/J2irVuxV8kEq8v3MAh49tlq56n0etAoeVZNI+4IJjLBgiAIgiA0uJSTMSBJhHXozLmoA8Tu2IrWzR2/UNd2X0OefgGmTnfpApEcm4u7l9ZZDlEb70AlCM5JSaJZp674BldmM/s+8jih7Try3znvkJN8nMgHWrF/1Tn0Bg3BLStboNmtVmTZgUZXGQRnfDgHXcsI/CdOxFbeV9frvvsw3NGDzI8+RhMYgP8f/kDJzp0YY2PRle8L4CgrQ9LrkTSNMxyryARnzpkDGg2aoCDn3CVJouOZ07WeG/zmm5hOn3LJEjc2jfNTvwXl5+fjV0uNzrWMEwRBEITbRXFeDkfWryK0bQdK8nKcx5tEtHKWPFSo6et+Y7EFL383VKrLtxX1CaqsYw1p046gFhHOn3uNHodW74aHrx8Jhw8w6uV7yTxfRHh719/ZVouyu5zWrbIcomjdOtR+fvhPnIg9NxeoqIWtrCnWt26NpNViOnkKn1GjAJCNRlTu7pedc0PSd+iAoVcvVL4+lGz7DVt6Ovp27ep0rv+TT9zg2V0/UQ5xicu1JLNarZSUlJCVlcXgwYNJTk7GaDRSXFzM/fffX61f8Mcff0xeXh4mkwlZlsnPz+fuu5WtFW02GyZT9b3JBUEQBOF2k3X+HFazibvHP0H/iU85j4e0blOn800lVpeNL2qjdXPj7vFP4OHrx52jxqJzNzDhvbkMmfoCOncDkkpFWMcuZCUnolKrGDmtK536VWaiLcYyzkcr7VIrMsGyxYK9oADL+fNYL17ElpcPKLWwXvfdh/cDDxD00ktIWi26iAjXcojSMlT1vNi+Pmn8/Gjx42KCpk93Hmusu79dC5EJvsTrr7/OyZNKi5bo6GjuqLLLSbNmzZg2bRpLly7FYDDwxhtvMHbsWL755htUKhWDBg2iV69ezJs3D4DOnTvz2GOPMWDAALZv345arSYsLIwhQ4YgyzKPP/44U6dObZDnFARBEITGorRA2YfLLyQU76AmdOw/iNO7d+AbXLedz0ylVnya1C2Y7Pvw4/QeOx5VeVemsPYdCWtf2cbL3cvb2aINwGG3c3zrRtr16cfBVcuI3rwOwFkTbKuyrXDJvn1gU9qqafz9URkMhM2tXKSn8vLCUVZl8wmjEcnQeDPBFXQtWwKgDQ9HFxHRsJOpRyIIvsSXX37pfD106FC2bt3q8r7dbuf06dPExcUxbNgw1Go1Y8aM4dFHHwXgueeUnoFZWVn079+f4cOHo9PpMBgMtG7dmm3btjF58mQiIyNv3kMJgiAIQiNVkJHO1oV/B8Dgoyy2at+3P6d376B51x6XPddmsXP2YAZFOSZadA287NiqVJdpS+ru5YWprBSHw45Kpeb8sSNs//4bdv34PU3btneOq+gOYcvOdh4r3bVLqZmVJOfCMZf7urtjL6kSYJeVoTJ4VBvX2Kjc3IhYvtwZDP+vaJRB8O5lceRcKLnywKsQ2MyT/uOvXMcSExPDyy+/jCRJHD9+nCFDhgDKhhYffvgha9asITo6moMHD9KuXTvatm1LTEwMXl7KasmKQv1jx47x0ksv8e233xIREcGSJUvYuHEjM2fOJDY2ltOnT/P777/ToerWg4IgCIJwmzmwcqnzdcUmGK3vjOSVJWuq1QNfKnbXRfauULZWrks5RF24e3qBLGMuLcXdy5vMxHgAbFYLF07FVM61IhOcrdQw69u2oXirsmGurkULl/6+FVQGA7asTOfPjrKyRl0TXNWlG3b8L2iUQXBD6tKlC7/99hu7du1i06ZNfPzxx6Snp/Pqq68C8NBDDyHLMqtXryY8PJygoCD27dtHYmIioOwOBzBs2DD27NmDJEk8+uijdOnShS+//JJp06ahVqvx9vYWAbAgCIJw2/P0r7mP7JUCYIAzByo3l9Dq62fTKbfyPrjG4uLyIDiBgPDmdBowmN0/L3KOq9jkypajZIK9R40i+wvl22SpljpflcGAo8zo/NlhNKL18amXeQtXr1EGwXXJ2N4oFZnc+Ph42rZVehOWlZXhVr4K1Gg0Mn/+fCIiIti7dy9arZY333yT/fv3M3/+fLZv3+681s6dOykoKOD+++8nKiqKw4cP06JFCyIjI0WHCEEQBEHAtcvC1chJLSY3tQTvQDeKckxYjLYrn1QH7p7KN7sJh/cTOeYR8tPTCGoewV0PKEmw3NQUTu/egc5dCXTthcpGGr6PPELJ7j0YjxzBrXPN2xZLBnesFy+SMFhpG2ZNS0MXHlYv8xaunugOUYu4uDiXIFhf/rXH3//+d0aOHImXlxfvv/8+nuWNpMeNG8fQoUPZtGmTs0vE0qVLGThwIBMmTMDNzY0tW7Zgt9vZsmULU6ZMaZgHEwRBEIRGxGZWOiUN/sNzV3Xemf0ZqNQSY1/tSdeB4XQbFF4v83ErD4J3/7wIc1kZxTnZeAU1QVKp6D32UUZOf5Up874iuJXSucJRVARaLeqAACJ++pEWP/9MyJ//XOO1KzpByDYbbt2UzTdMcXH1Mm/h6jXKTHBD2bJlC2+88QYajYakpCQ2btyIm5sbZWVl5OTkcPjwYV566SUmTpzInj176NSpEydPnmTjxo1ERkbSuXNn9Ho9mZmZ6PV6EhMTadOmDampqYCyqK5JkyZs3ryZadOmNfDTCoIgCELDs5pNaN3cuWPEA3U+5+zBDI7/doFWPYLw8ndjwIT6+wbZ3buyPOH0np3YrBa8Ayt3PZMkicBmLZw/2wuLUHt7O79JNvSs7Cp1qYog2L1HD8L+73OymzXD0Lt3vc1duDoiCK5i0KBBREVFoall5xaHwwGASqWipERZuJeXl0d+fj6PPfYY06ZN46uvvmLPnj1otVqGDh0KKL2HLRYLL774Ih06dCA+Pp6ZM2diNBoZPXr0zXk4QRAEQWiErKbatyCuTVZyEQB9x7Wu9/n4Bodw/0uv8+s/v+DYr+sBZbvm2tiLlCC4LlTlJRQqd3ckSaLJa69e/4SFayaC4Cq0NazkrEpVZZeanTt3AvDMM8/w7LPPOv8FOGPGDOeYMWPGANCqVSt+/vlnl2v985//rI8pC4IgCMItzWIyonO7ug4JNqsDd28dvsE3ZqOJjvfcS9KxI5zapazzqdhuuSaOoiJU3l51um5FxwjpGuughfolaoKvk0qlctm/XBAEQRAEVw67nY1/n8fFM6eqvWc1X30m2G5xoNHe2BCmda/KMoWAKuUP1eZSVITau24dHuTyLZdVIghuFEQQLAiCIAjCDZUcc4zTe3ay5rMPq71nNZvQXGVQaLPab3gQHNGtsrZXU8s3xfbCQkyxsai96pYJdhiVRYCSuwiCGwNRDiEIgiAIwg2VePQwAMaiQowlxc42ZKAEwVddDmFxoNHVT1/g2ujcDYz+0yx8g5vWOiZzzkfKfKrsGnc5+vbKAj737t2vf4KNQXEmnF4Ld02FW/BbcZEJvkRFezOAxMREcnNzq42x2WzY7fYaj9ts9dOnUBAEQRAaSnZKMRdO5dXb9UoLi5yvz0UddHnPZjJddTmEzWpHo7vxIUz7vv2drdBqYo5XdpPzHnV/na7nPXQorX/djNegQfUyvwZ3chVsnAmFqQ09k2siguAqLBYLgwYNYtWqVQC8+eabfP/992zbto1t27bx66+/kp+fz7Jly7jnnnvo168fbdu2pW3btvTr149+/fqxZs0aQAmICwoKKCgowGKxAJCZmcmTTz5Z6/2nT5/OggULmD9/PrNnz77hzysIgiAINVk25zBr5x+7pnPtNiv/eeNFzh874jyWnZSLpA7CzdOfuAN7XMZbzCa0+qssh7gJNcFXIttsWDMz8Rk3Dr8JE+p8nq5F7fXFtxxrmfLfoosNO49rJMohqtDpdKxatYp58+bRqVMnysrKCAkJISND2ZbRYrFgNpuZOHEiEydOBODf//43AFOnTnW51oEDB3j77bfx9/dn+PDh7Nixg9zcXE6fPs2IESPo3Lkzn332mcs5Pj4+GAwG9Hq9M3AWBEEQhFuJqaSE7OTzpMefpWWPOwGwWUyAFr/QziSf2IOptARk2L/iZ4qys2jdM7LW69mtDiwmG+5eOucxm8WBwVtX6zk3WubcueQv/hHZYsGtY8cGm0eDsykL/UQQ/D8gOjoaDw8P3n//fUaMGIHD4WDRokXO95988klCQkLqdC29Xs/AgQNp06YNVquVX375hZycHGbOnOlyzezsbF544QWWL1+OTqdDpVI5+xHbbDa6detGdHS0c8c6QRAEQbhZbBb7Vdfe2so7IJhLS5zH7DYzkqTFM7AL6XE7STi0n+zk80RvXk+nAYPpPW58rdfb9sMpEqKyeOHrQUgqpe70ZiyMq41st1OwbDlunTvj9/gEvIYNa5B5NAo2o/LforSGncc1apRB8I5FC8lKTqzXazZp0YpBTz172TFarZZXX32Vp556igULFvDuu+/y/vvvs3LlSkaNGsWyZcvIyclh0KBBzqA0u7wYfsGCBQCYzWZ+/PFHl7Zpp06dYsiQIciyTFxcHA888ABlZWWsX7+eoKAgzpw5Q0JCQrX5HDlyhFatWokAWBAEQWgQxhIrXv5XFwRbzUoQbCopdh6z2ywg+aDSNMU7qAm/LvgSgJDWbRnxwp8ue72kEzkAFGYbnX2B7VYH6hu8MK42xdt+w1FcjN/Eifg8cJtveFWRCS4UmeBbXpcuXVi/fj0FBQVMnz6dHTt2cPHiRYqLi1m6dCnZ2dn06NGDmJgY5znDhw/HYrGwY8cOl2tFRUU5X3fu3Jm5c+fSv39/8vPz+eabbwgNSbkhTgAAIABJREFUDXW+P378eJYuXVptPitXrmTSpEk34EkFQRAE4cqMxRa8/K+yXrciCK6SCXbYLCBpsZTZGDfrb/wwczoAXlW2I66NX4gH2SnFZKcUO4Ngm8WBtoEywVmffYbaxwfPAf0b5P6NirUiEyyC4HpzpYztjRQVFcUvv/zCd999x7Rp03j66aedO8XFxMQwYsQI59iYmBhUKhV33nknK1eu5KGHHnK51qJFi3B3d+eNN95g+/bthIeHY7PZeOGFF1i1apUzWzxu3DhmzJjBwIEDnec6HA7Wrl0rFsj9P3tnHR5Xmfbh+4xbZGKNNnWhRgVaaCnuxaWwsDgstsDCIgvsxy62yMKy6LYs7rC4tKVAgbpRt7RJ07hPknE93x/vSKaZNGmb1HLu6+o1R95z5p3pTOZ3nvM8v0dBQUFBYb/htvt3+xh/OB3C3SYSHAp6kTRaPE4/GQWFnHTdLfzw35eQ6NxWKyXTSH2ZneqSFgYf0QcQaRr7IxIsh0L4q6pIv+Ya1Clda5BxSBPNCT440yEUd4ideOWVV7jkkktwuVxYLBZGjRrFkiVLcLvdGI1G1GrxpautrWX69Ok88sgjPPDAAzz22GNx0d9AIMBVV13FE088gc/n4+677+a+++5j6NChDBs2jHvuuSc6duTIkfzwww9x81CpVKxYsQKjcfe8ExUUFBQUFLoLt333i7QjkWCnzcbaH2bz+ZN/JxR0A0IEAySHI8DaLjTJiFiXbl9djyzLyLJMwJ/YHcK7bRtV995HKDyH7ibY3AyBAJrMziPYvYKAaP6hiOBDgNLSUrZv386ECRPwer1s2rSJCy64gO+++45bb72VZ555hpaWFj755BOmTJnCI488woQJE7BarXzwwQdcc8013H///Wzfvp309HTGjRvHaaedxieffMLNN99Mbm4usizz6KOPsm7dOt5//328Xi9+vx+Npn1Q3mw2EwgE8PbQl1lBQUFBQSEh4QCt39veE78zIpHg1vpa5r76IiWrRIBIkrR4nAFCIZnCMWOZfPHlTL38mk7PF/SLYnGHzYvD5iUYEOuJfILrX3yJli+/xD579m7PuytEmmIoIjhMRAQ7aiB48PVJOCDTIfYXlZWV3HfffQBkZ2dHo7NFRUVceumlfPHFFyxdupRPPvmEWbNmMWhQzEB7yJAhLFq0iBdeeIHGxkYmTJjA4MGDAXjyyScZP348VVVVeL1eNBoNn3/+OUajkddee42ZM2eiDbdkbBsR/uKLLwgEAlx55ZXcdNNN++ptUFBQUFDo5UiSJCKuvtBuHxvYKXAz+oRTWfvjbCSVGjkkU7u9lZyBKUy6oGveugF/bA4t9W60enFHVqNtnw4RcY9wLFxIyjnn7PbcO51LXVgEZykiGIiJYDkkhHBK/v6dz26iiOA2TJ48OeH2wYMHs2jRIvR6PQUFBZzSgR2KxWLhL3/5S7vt48cLn8Tc3Fw+/PBDgGiaw7XXXsu1117bHdNXUFBQUFDoFiRARliR7S7+nURwao4oBE/OCOF2SWxfXU/OwK7n0wYDIZIzDLQ2eGipc2FJFY5JelN7CeMr3SGOaey+bndtqfzznwElEhzF7yH6aWmtOuhEcJfSISRJSpMk6WRJkjJ6ekIHIpIkKTZlCgoKCgq9AlmWCYVEHm7bKGxXifgER0jPKwBAZzSQP9RKSTi3t6sE/SFSskyoNBItdW5a6oUjQUpmfM2MHArh3b5dHNPa2u48e0uwpYVQSwsA2i72DDjkCXggOU8sH4StkzsVwZIkWYFvgCOBeZIkJbz8kSTpNUmSFkuS9OCeTmZ3vhS9GeV9UlBQUFDoKSI5tyBcGBKO8YcIBhML5J0jwf3HTkBrOZ3C0afT//BMWurdNFU748YULa+J+gEnmo9WpyarbzJFy2upLxeuEylZprhxgdpaZLcQyMGwWO1OfOVC5OW98DxSOIWx1xPwQvoAsXwQFsd1JRI8GrhTluXHgDnAuJ0HSJJ0PqCWZfkoYIAkSYN3dyIGg4HGxkZF4HWCLMs0NjZi6EJFrYKCgoKCwu4SbBP97SgS/Nqf5/PFM78B4nfpl3dfZ9XsrwkFg+0iwaGQjFo7HL3JQP/R4obyhw8vo7XBHR0z97WNfPvy2oTPFfCHUGskJl84CGezl6VflqA1qDEmxQtRXzgKrBs4MBqx7U78FUIE6/IPrlv+PUrADZY+oDUflCK405xgWZZ/AZAkaSoiGvxwgmHHAR+Hl78HpgBb2w6QJOkG4AaAvn37tjtBfn4+FRUV0Q5sCh1jMBjIV76ECgoKCgo9QFvhGwwXxgX8QZZ/W8q4U/qiN2nxe4PUlLQSDIQoWbmEFV9/BsDaH2Zj2sk/d8e6RgDUGjXmVD3ZA1KoKWlh28o6xp1aiNcV8yIOhWRUqnjv4KA/hFqrIntACsMn57BpYTV9+iXHdWYF8JYIEWw8fAwtn35G4xtvknr+ed3m5+uvKAdAq/z+xgh4QWOA5FxoPfjSIbpUGCeJT9p0wAYkcs42A5F2IU0kiBbLsjwTmAkwYcKEduFerVZL//79uzZrBQUFBQUFhR4h6A8hhzz47O/RUncxcBhbl9fx2+wdBAMhjjp3YHRsY6WD0jW/YbAkcfINt/LLO6/TUL4DJAnCd3Zn/Ud0WY1Yml1wz3g+fHQZZRubGHdqIY1VsdSIljoX1mxz/HwCIdRhJ4ijzx+Ezqjh8BML4sa0fv89tY8+ispsRj9QODfVPfkk/spKsh98oFveF19FBeqUFNRJSd1yvkMCvxu0xrAIPvgiwV0qjJMFtwBrgbMTDHEAkQx1S1fPq6CgoKCgoHBgEfCHCAWrkUMtOJqE20KkaYar2YvHEYuFuVp8NFVXkJabz5CJk7nq2ZeZPP33HHXBpe3O625zXFqOGXuTsNeq3xHrLGerdrU7LugPodEIWWEwa5ly4WAs1viUwMrbbgdAW1AQF/lt/vhj/LV1u/cGdIC/vAJtQUHnA3sTAS9o9MIV4lAUwZIk3StJ0hXh1VSgOcGwlYgUCIAxQGm3zE5BQUFBQUFhnxL0h5ADNQD4PcJlwRYuZGuuc8eJ2W9fXkv11h001+nYuqIWrU7PpPOnc9SFQgSrtbro2LaWZgazFm+4e1x1cTM6o9gXcX5oSyAQQq1N3F7ZX1VF7T/+0WbyQdQpydFVORik8bX/EvL5aJ09h5Bv9zvgRfBVlKMtUFIhooSCIidYaw5HgiuhsXh/z2q36ErEdibwe0mSfgXUQIUkSY/uNOaL8JhngYuBb7t3mgoKCgoKCgr7Anujh1AwXgRHUhbqy+zUlMSKzmTZS9Bvx+dNYvuamLuDJEmcfef9nHD13wGYeskQRh2bF91vMGvwugPYmzxUbLbRb3Q6epOGloZ4ESzLMqGAjFrTXq7IwSAVt99B01tvR7dpCwowjBgRXU85+2xsb7/DlsPHUnnHHdQ+/vgevSdyMIi/qlopimuLNxzBNyQLEQzwQrts2AOaTkWwLMs2WZZPlmV5qizLN8uyvEGW5Qd3GtOKKI5bAhwvy3L3l2UqKCgoKCgodCs+twtXa+wnOxgMsfjLYuRQLQABXytySMZW46T/GCt+19f8/O6v+Jzf4XPOJuQTkb/Mwr4018anMgyeeDRejwWAoZOyUaljksNg0YIMX/5rFcGgzPjT+pGSaaRkVR3vPLiI4lUihSFi16bWtpcrja+9jmfdOiSdiDabj51KzmOPos3JQZuXh37oUFLOmiYGh8R5mj/8CPtPP3X4frjXraPsmmvxVcQXeQVqa8HvR5uvpENE8Ya9mPVJYGrTRsLfPpp/oNJtHeNkWbYRc4hQUFBQUFBQOMD58p+PUbZ+Den5fRl90mks+/JrXK0yctAFkoqg305ro4eAL4TBVEHQu5VQoAI5KIROyLcRAJOtmTrZjizLca4NjZVOkjMM6AzxckNvEvZmLfVuTr1+JGk5ZvKHpbF5cTUOm5fKzTYGjs2K2rXtHAlu+fob6p99lqSTTyLvX//CPnculhNOQBVubDVw1neA6HoHYBg9msJ33qb00kupeehvWI49Fkkd33Y52NpK2TXXErLbsb3zDn3adIB1rVwJgDY/D4UwnogITobskbHt85+FE7qnGLGnUQrYFBQUFBQUein2xnqsufk0VpQx782ZOG11GCwmcocOx5pzOEG/nZLVNYSCzWyY9y5AVABHkADjhjX4/eBqjc+5bapykJ5nafe8BkvM43fQ+CwAjjpvIFc/NYXUPibsNuE1HLFr0+wUCXavXg1A7lNPIWk0JJ9+elQAA0g6HZJOh0qnY+Cc2fR9/XVUej3p115LoL4e95r2nsS1Tz5JyOVCk5uD/YcfY8+1di1Vd98DgE4pjIsRiQQbkiFtAFwXfs9+fQqq18DPT4bbKh+4KCJYQUFBQUGhl+J1uSgYPhKLNQ0ASZ3F9Ice59KHnyY1ZzgQYtGny9HrViCHglxw/8PoDPHtio0hMLtE+kLblIiAP0hznTuhCNaHC+GSd2p9DGCxGnDYhHiqK21NOC7ksKPNy0NlbH/8zugKC1FbhO2aZepUJIMB23vvxY1pnTWLlk8/I/2aa7BOvwR/ZWW09bK/TWqENien0+frNUQjwWE3jsyhsX2vngg/Pw4fXwHBRM66BwaKCFZQUFBQUOil+DxutEYjyZl9AFBpMkjLFYIxo6AfABptMyp1Pf3HTqDfmHGMmDQFZJn+dcIsSu92kxzWuY1ba6LnttW4kENy9HxtSUoXFmdHnNmv3T5Lmh5HOBJcvKoevUlD3lBr3Jig3YFqD/x61UlJWC+9lNbvviPocAAQcrupuvc+9MOGkXHrLRiGDwPAu2WLeK7WmIWb0i65DZHCOH1S7PHBehh6BoTCwnfrHFj9XuLjDwAUEaygoKCgoNALCQWDBLxe9EYTecOFo4LOPDSa0zvp3PGotXrSs2twNNaTkiWE8oTxR3HUtkqS3UKoqoMy+df/DlXQR+Pmyuj5I7ZqiUSwOUXPzS8fz7BJ7SOrSWkGPA4/Javr2b6mgf6jM1Cr4+VKyG5HZWl/3q5gHDcWZJmSs86m+PQzKL34YmSfj4wbrkel1wt3CbWa1jnfAxBoEK4Xhe8fuGJuv+ANF1QaYpZ0aHQw7or4cVtmwYrXoalk382tiygiWEFBQUFBoRfi84jcXp3RyFEXXsqIE/+KOXVwdL/OqOPIc85nx9plBAMBArPmUDR5Ck3PPIvV5RXJwIBOqyX1nLMxeRqwVbZGj/e6AgAYLTGv4LZIqsTevyOOyUWjVzPvnc343AEGjMtqNybocKC27FnnNv2AAQAEqqvR5ubi3bpNvI5w11pNejqp55+H7aOP8JaUEGioR221Yhp3cNl/7Taz7oNfn+76+LaFcW0ZdDIk58Po6TBsGhTNhm/+BF/d1n1z7SYUEaygoKCgoNAL8blF/q7WYESr04NsQht2cQi53ZScex4jCgZiDucLq7dsJdjYiHvjRlCpGDBhEmkON4cXDESl12PRemh1xIRtwJe4qK0zjBYd+UOteJx+tAY1BcOt7caEHHuWDgHxxW0F/301tr2wMLqcefvtqIxGKv/8Z5o//AjJGN+h7pBDlmHpK/DTo1DdvmiwHaEQbPgM1HrRNrktag1c9wOc/hSc+hhc8BpMvQdK50Ptxp6Z/x6iiGAFBQUFBYVeiM8tIsF6k0mse4PoDMI2zFtSgnfzZhof/wdTf3cVEmDx+ECjYfAvP9Pvo4/Imn4Jk4qryD72OABSM/S4sBDwCoeIgD8IgFq3+1IjNUsIq36jMtBo1e32h+x21EntC+66gqTTkXn7bfR9/TUkSSLz9tswTZiAKvw+AGgyMsi68068GzcBYBg2fI+e66ChNZbGwoxjOh/fUAQ16+DI60FKENFPzgFjKlj7wagLYcqf4IZfoM9h3Tbl7qDbfIIVFBQUFBQUDh4ikeCI24PPHYiK4EhziZDTyWFTT0D3+luo+qspfP991BYzmjQRHe7/5Zfoh4gUirQBmcir1TSu2EyfyaMJ+ENIKqldPm9XSO0jBOnAcZnt9smyTNDhQLWH6RAAGTfdFLfcdj06h4svwr1qFZrsbNKuunKPn+ugYONX8esBL7x2Cow4D6bc0X58U7g98ojzu3Z+nQlyD9+7OfYAighWUFBQUFDohUQiwTqjidrtrXhdASypwms3ZBeV/yGnKG6TGhrR9R8QtRqLYBg6JLqcOXYQrC6j7ret9Jk8mqA/tNupEBEGjsvC1eqj36iMdvtkjwcCAVSWPYsEdxVJpSL3ySd69DkOCGbdJ1IhMoZCg3DEYNsPUL1a/EsogsNFbukD9t08ewAlHUJBQUFBQaEXEokE22r9/O/JFTTXutCGI8FR+7CwCA42t6BOTd3l+dJH9AWgqVh4Bgf8ITThVIhgSwvlt9wa7bzWGQazliPO7N+uUxxA0GYDQJ2c3G6fwm7iaRUCeNTFcNNCuHGB2P7h7zo+RpahbAkY08DYPl/7YEKJBCsoKCgoKPRCWuqFWN2yNObooAqnLoTsQgTLfr9IP2huRm3dteAxWnTo8NLU4Oe9h5bQXOvCYhWR5YZX/oPjxx/R9snCNH78Xs3bW7IdiLk5KOwFbnFBwYBjQa2FPiPhlEfB0wKrP4DWCtH1TRsuDAz64ctbYfM3MPn2/TfvbkKJBCsoKCgoKPRCarYVYbZmUlPip99okXbQUi9SJEKOWIMIf1kZBAKdRoIBkswhmtR94jrHAfhrRRONkNOV6LDdwlcsLM30gwft9bl6PRERbAj/30oSHP1HOOFBOP1Jse3Hh2Pji+bA2g/h2HvhpL/v27n2AEokWEFBQUFBoRdSu30bKk0fdEYtp1w7gmXfbGfwBOHJGwxHggEcv/wK0CURbM1NotEZs8wKBsIFduHz+Wtr93renk2bUaemRovzFPYCj+j6lzCtYdiZkDYQKpbHtjXvEI8Tb0zsCnGQoYhgBQUFhX1EyarlSJKK/ofv3e1gBYXuwNXcjM7cj9zBqWj1aiZfEIushux2UKmQtFoaX38d6JoITh+cw7at5dH1oF+I4KBdpFwEOhDBTW+/jWQ0Yr3oonb7fKWlOJcsxfbBBwQaGgi1tpJ85pldf6EKHROJBCcSwZIEOWOgek1sW2sVaAwHfS5wBEUEKygoKOwjPn9C3D688IFHKRx94NkFKfQeQsEgfq8HjVGLztheCgQddjQZGfT564NU3nkXAGpr5yI4rSAViInggMdPyOuNiwTLshxtzRyh9vF/AJByzjmodPEd5moefQznAlGwlTxtGoG6OjJu/EPXX6xCx0RFcAf/t6Z0cDXG1u01kJR9SESBQckJVlBQUNgn2BsbossLPnqbLYvn8/L1lxEM+PfjrBR6KxF7tFBIg87QXgT7SrajtlpJPvlkCl55BdOkSegHdZ6Dm9InvntYCBUtn38uIsGShOxy4d2yJW6MLMvRZdfy5eyMyixs2bL//nfy/vk0hW+/ha5fv07notAF3LtIhwAwZ4iUiaBogY29GpJy983c9gGKCFZQUFDYB9QUFwGQM2QYDeU7+PmtV3G3tuAM2z0pKPQknz7+f6z9cU50PWKPFgxoYw0ywrjXrcO9ahWpF4hGCJYpkyl88w3UXWhTnJppAuS4bY0zXyXU0krKeeeBWk3rd7Pi9occsfxj37Zt7c4p+3zohw3DOv3iTp9fYTdpKhbpDTu3Po5gShePkYhxa5XoBneIoIhgBQUFhX1AS7g6fuD4iQS8XrwuIUKcLb1HBHucjs4HKXQ7fq+H0jW/MXfmC9Ft3rAIliRdu3QIx7yfQaUi5eyzd/u51FoVFnP8rXJ/VRWyz4eubwHmSZNonDmTbaecirdYdB1rmyfs2bQZ97p1hMKRahD5xF0R4Aq7iaNe2KBlDO54jClcfOhqBJ8TbKWQMaTj8QcZighWUFBQ2AfYmxrR6g1kDxQ/OH6vB6DXRILX/fQ9L11zCTvWrt7fU+l1RC7A2uILX4Qh6dulQzgWLMA4enSXCuESkdrHhDroja7rhw8HQJWURPIZpwPCdq1hxgwAAnV1sbl+8QWlF11M01tvR7eFWu2olMYY3Y+9GuQgHL0Lv99IJNheBbUbARmyR+2T6e0LFBGsoKDQJWRZZvvaBr769ypKVtXv7+kcdDgaG7CkZ5CWmx+33dnctJ9mtG9Z8tlHAKz45rP9PJNDg2DAH72QAtg0fx4b589LONZWWx1driraDIDXHW6HLOmiXeIAAjYbnnXrMB8zZY/nNuzYQvLtwlFAE/SQ8YcbxLLVStJJJ0XHeYu2isdw84vsvz1E2pVXoM7MwFtUFHutdnvXI8Hly+DNaeB3dz62t+MTn4FotDcROWNAnwJL/gNbvxfbFBGsoKDQ21g5q5TvXl5L+SYba3+u2N/TOeiwNzaQlJ6BxZqGRq+Pbnc2H/qRYK/LSWt9LSq1htK1q7DVVO3vKR30/PTGDD76218AaKwoY/Yr/2beGzMSFlo218RE8Ad//TPQNhIcnw7hXLgIZBnLMcfs8dyGTsxh4pQkRq99maPKXiPp1FPJf/EFLCecgDolhYzb/ggqFb6yMhwLFtI6axbaggKsl1xCn7/8BcOw4fhKS6PnC7W2okruoghe/l8onQ/1m/d4/r0Gf1gE68wdjzFaYepdsHUO/PoUHHYOpPbdN/PbBygiWEFBoUvUltqxZpsYfUI+1cXN+H3B/T2lgwp7Yz1JaelIKhXWnLzo9mVffMKWxfP348x6nrrtIvfz5OtvQaVSseb7b/fzjA5uZFmmeMVSGspLCYWCfD/jBWQ5hMfpoGzdmnbjm3e66Fj57ZdRdwhJ0sWlQzjnz0edmophxIi9mqM2J4eMpg0Y7DVIkkTSSSehMojWu5k330zGjX9Adrkov+463CtXknTC8dFjdf364SstRZZl5GCQkNOJOqkL6RBBPxTNFsuNxXs1/16BL3whtCsRDHBkGzu64x/oufnsBxQRrKCg0CU8Dj+mFD2ZBUmEAjLuVh9Bf4gPHl7K1hV73wXqUMbrcuGwNUXFb1sRHAwEmP3Kc9ibGjo6/KCnqaoSgMLRYxk8cTLr5/2A3+Pp5CiFjmisKMPZbCPo97N6zndUFW3ipGtvRm8yJ7ygaq6pJmfQUK58+kVAWPR5XeEooKSPukPIoRCOhQsxT56MpFa3O8/uoMnKBCDkStwmWZ0h2jSr09IY8M3XZN1zT3SfccwYQi4XLZ9/QbBZWHipuxIJLl0Anhax3FSyF7PvJfjD/zda067HaQ1w1XeilXLm0J6f1z5EEcEKCgpdwuP0Y7Roo/mDPk+A5joXTVVOvv/vhv08uwObxooyANLzxW3EtJx4n82A18uCD94mGPAjyzLBQIBVs7/G7/O2O1f0GJ+v5ybczTibm0CSMKdaGXvqNLwuJ5sW/Ly/p3VQEAwECIXi77qUrY9Fe9f9OBtjUjKjTjiFAeOOoHTtqnbnaK6tJjU7h4y+/TjlD7cR8Hqp3LIRSaUBtBgsWgC8mzcTbGjYq3zgCJpMIYLlDi52NOkREWxFP2hQnOhOPuN09IMHYfvgAypu/SNIUueRaVmGzd8IQWfOgvotux6vEMsJ7iwSDNBvMky9u2fnsx9QRLCCgkKX8Dj9GMza6K1TnztIS10bG6Nwe1SF9kRFcIEQwW0jwQB9Rx3Oxl9/4rnLzmP5V59SvHIpP70xg+IVSxOeb/OiX/n378+nZlsR63/+AVdrS8++gL3EabNhSk5BpVaTO3Q4Wf0GsmrON3FNEhTa43E4eOWGy5j14rOs/XEOH/39PmRZpmz9GlRh0dhQvoP8w0YiqVSk5/fFaWuKi7IH/H7sDQ2kZgtv17xhQkyWrvkNrcGCWq3ClCQ6tDmXLgPAfPTRez33iAjuCHWKSG+INMJoi6RSYT5mKp516/CsX0/ec89hmjBh108473GRD9z/WOg/Fbb/AiHlb9Iu2R0RfIiiiGAFBYVOKV5Vh8fhx5AgEhzB2dJx1LK301ixA41OT0pWHwCsuUIEjz/zHK54+kXOvvP+6NjNi35l+6oVANjCaQQ7U7RYtJB974E7mfPKc7x9960HXLGZz+Pmk0ceoHjlMhzNTZhTRUcqSZI4/LQzaSgrpWLT+v08ywOboqUL8TqdbF74C3NnvkDFxvU0lJVSvmEdAydMjI477JgTAKKfr5a6mCVaa30tshwiNVvcfbDm5GJKSSXo96PWmDGn6nEtXcKO31+Bfe5cVElJaLOy9nruamsHHcjCaHPFfFLOnJZwf8pZ09APG0bBf/9L8qmndP6Eaz8Uj5NuhMGngLMe6pQ7VLukq+kQhzCKCFZQUOiU2TOEWGkbCfZ7gnEi2GFTRHBHNJSXkZaXj0olLiAikWC9yUJm337oTSbOveevAFisaTERXJ1YBDdVxbtzBINBPnn4gQMqstpcU03Z+jV8+c9Hqdy8AbM1ZsM0bPKxGCxJrJ79zX6c4YFPXWn7vNblX32Kz+1iyMTJTH/oCU6+4VYGHTEJgNQ+ItrbXBfL0Y84Q0T2SZJEfjgaLKlM6H3NlF19Da7ly3H/9ltUnO4tkkpF1n330u/jjxLu1/Xty+BFC7H+/vKE+w3DhzPgi88xTzyy8ycLhcDVBBNvhAHHQfpAsb31wLowPODwOUW3ONXe5X8fzCgiWEFBoct4nP5YOoQnQHOtC4NZ5BM6mpVCp45oLN9BRn7MVshgtjDtjvsYecLJ0W0Dx0+k/9gJbF+1AmezDUmlihaUtcXjdETTKwAmnHU+R50/HXtjPW57a8++kN3A5xGpMpa0dLxOJ+aUWGRQq9Mz6oRT2Lp8MfbGBn58/T/MuPGK/TXVA45gIMD899+Mc9FQa7Wo1Bo2LfgZlVpD4eix5B82ktEnnhYdkxJOeWj7+Yg4Q0TSIQDyhgsRHAoZ0dbtwDhmDKaJIrLcXSIYIP2qqzCOHt3hfk1aGpIkdbi/y7SUg890KAZmAAAgAElEQVQBmcPEeqTBg/PQLTbtFvyuXh0Fhi6IYEmSUiRJmiVJ0veSJH0uSZIuwRiNJEllkiT9HP536DgpKyj0ctpGF4dOyo5Wkvs8Iic4b6joKqVEghOzZfECHLYm0gsK47YPPWoKSWkZcdssaenR5f6Hj0/YUrlmm2gikD98JADW7Fws4SIjR1Njt859b/CHLbjOuOUujjz3IkYcd2Lc/jEnn4Esy6yZO4vVc77BYWtCVnI4aa6p5sOH7mHZl/9Do9VRcJj4OR047kj6DBARzn6Hj8OYwDLMaEkiPb8vW5cuYslnH1G5eSNN1VXojKa48ZG84FDQiNbbjK5//2iL5MAB9BnqMhFP4KzDxKM5/L1yNUDdZtg6d//M60DH5wSdZX/PYr/SlUjwZcCzsiyfAtQApyUYMxr4QJbl48L/1nXnJBUUFPYfPncAgMkXDiI1y4RWL0Sw0+bF1eojs28SOqOGxkoHAb/iHdyWmuKtfPPcEwBk7CSCE2GxChGc1W8g1pxcvA5HuzHVW7eAJDHtjnuZetnVjDjupOhxDtuBI2B84eIsQ1ISx1x6ZVTMRUjJ6sPA8Uey9PPY7fKXrrs0rgtab2PTgp95577bsFVXctaf7uP2dz/jpOtvQZJUjDrxVNRacdelcNThHZ6j/9gJ1JZsZeFH7/Dz269St30bWf0GxEVcMwv7MfSoqSAVonG3orJYSD5rGqYjjiDjxht7/HV2K1Wr4P2LxXJWOBKss4BKA3P/D145Ct67UEmNSITPCTolErxLZFl+WZblyGVUJlCXYNgkYJokScskSXpNkiRNgjEKCgoHIa5WYcVlShY3gSSVhFavpni1+FOQmmUiPc9M0dJavnpu9X6b54FI25ze9PzOuywNOmIShaPHMun86ejNFvxeD8FAIG5M9dbNZBQUYk61csTZF6DWaLCkiXzbAykS7POIfHGdwdjhmLGnnhW37nU6qSneGretYuP6A67or7vxedzMfvlffPfCP8no258rnnyBIZOETVlabj43v/Y+/caMi0bKswcO7vBck86/hAsffJQjzrmQmuKtVG/dQvagISz4ZCslq0W7c5VKzfFX345Km4/GZUNlNqPS6Sh8522Sjj++w3MfkCz/b2zZkCIeJQlC4e/NsDPFY6SJhgK8eyEsnSHyqA2p+3s2+5Uu5wRLknQUYJVleUmC3cuBk2RZPhLQAmckOP4GSZJWSJK0or6+fo8nrKCgsG+JiGBjciwTyu8N4mgS6Q+pfUxodSI6XF18YFt17WsaK8qjy8kZ8ZZRPk+A2u3xObxZ/QZw4QOPMHji0ehNlvA5yvh+xvPMuPEKWhvqqd66hZzB8Yb15tQ0kKQDSgRHbLq0xo5FcN9RY9ptq94a83f1+7x89uTfWfTxe90/wQMEORTiw4fuZcOvPzHpgkuY/tA/SM6Md2cwmMVn4eTr/8jhp04je+CQDs+nN5koHHU4gyZMim7LHjCYNT+WM+s/62isdFC7vRWPU7RX1nrtqCwHsUVWTfjG89F/TLz/vBlgTIPqtftuTgcyrVWwbS78+Ihwz4gUEfZSuiSCJUlKA14ArulgyFpZliPNyVcA7S5TZVmeKcvyBFmWJ2R24h+ooKBw4OC2ix9LoyUmgk0pseWUTCODJsR+tINBJa8zQlNlORq9nkv+/hSSKv7P7fJvS/nfkyuo25G4mM1gEcLnnXtvY/28H3DYmihaPB+P00FW4YC4sWqNBmt2DsUrljH7lefwONunUexrIm15deFWuYmQJInfP/k8Uy6JFcVVFW2OLu9Yswq/x01L/aHbkdBha6K+tISpv7uKyRdfHvX/TUR6fgEnXnPjLsdEyCzsF11OzekfXf7wkWX878kVeJ0iUqoNuBJ69e5zZFkI1Y4cTtb9D5rL4rc5G8UxJzwIpzwav6//VEjtKzxwzZngOnAuEPcr5cILGp8d3DZFBHc2IFwI9wnwF1mWd3Qw7B1JksZIkqQGzgXaNy9XUFA4KPG6hAjWm2JZTlc8FjPT1+jUDDsqh6mXiOhUa70bBYG9qYG8oYeRN+ywdvt84fd1w/zEt/oj0T+AyZf8HrVWG+0GZkptfwuz3+HjqSstZsPPP7B54a8A1JZs4/2//hlPgtzinsbvcaPWaFBrtLscl9VvAIOOOCq6Xr11c7QYs2jpQgDsDQf/3UOPw8Hsl5+jubaGis0bog4OLbXC0zezsP+uDt9ttPrYxYdK076IrqFCfCa0fifqA0EEb/8VZhwTn94Qoeh7+PRamP2X2LZNX8PTAwAZBiRI4bjiK7gtLEXMGeLWf3cTCkHNepFb+9opMPeh7n+O7kKWYft8KPkZNEYYeYHYnta7RXBXcnevBcYBD0iS9AAwD9DKsvxgmzEPA+8DEvCVLMs/dPtMFRQU9gtel4gYtRXBao2KUcfmEQwJsSJJEn36ix/apmon1uwD4Ef1AMBps5GWm59wXyTgVbW1OeF+vSn2Hqb2ySGzbz92hEVwImeAMSefQenqldiqq6jYuI7DTzmDyi0bqS7azNZlixh1QhcaDnQjPo8brbFrRTeRqDeAq6WZ1vpaLGnplKxcBpKE02YjGAig1hy85SY/vv4Kmxf+gkanY/287wmFQow8/mR8LpE7ndInu9uf86TrbsHvceNqERdcfUekUbZBiMHta8SFhcbvRGU5ABwCPOFUqt/egiOvj9+38QvxaI81AeGTq2LLuWPbn0+SxD8AUxo0bG0/Zm9Y/xn88DdobhMbLF8KJ/7fgem7u/5TcSEBopnIyQ+LvOl+x+zfee1nulIY94osy9Y2zg9/30kAI8vyelmWR8uyPEqW5Qd6broKCgr7Gq8rgCpcDNeWqZcO5fjLhkXXU/sIwdNU5WTOq+up2tre3utQIxQM4nW5Eu6TZRlnsy2uSURbIjmZzbUu3A5fu/1thaElLZ1JF1waXU8kgtPzCrjmuZmMOPZEdqxbTSgUxB1up7xl8fyuv6huwu/x7DIVoi2R16oNF9FVFW2mbN0avC4ngyZMQpZDB1S+8+7QUlfDf/94LZsX/oKkUrFm7ncEg0GGTz6W9T/Njf7f7Jwz3h2MOfl0Jpx1frSb45SLBjPlosGoNSoqNovvpzbgPDDSIQJhi8WadeDd6c5Fg7AFpH5LrBVyWjglSKXpXHSaMro/HWLJK0JEDjo5fnvF8u59nu4g4IOf2qSLjDgPUvLh4rfBnN7xcb0ApVmGgoLCLvG6/OjNmk5N7XUGDZY0PZVFzWxbWcfnz67aRzPcf3w/8wVevPrihJ3aPA47oWAAS2ri9rEepx+VSrynNQkKCo3JKdHlpLR0+o6MNR1IJIIjFI4ei8dhZ/Wcb2mpFw4eZevX7PNGGj63e5fOEG1Ra7T87tFnuP7F19DqDVQVbWbH+jVotDrGnS4cJGpLujmSt49Y+PF7tNTVkjNkGEecdT4A/caM4/Rb7+LWNz5k6uXXhF0+dp02sje4WsIXWSsXMObEAnKHiHQavR40Qe++E8FlS8BrT7zP12b7b2/HlmVZiGBJJca0hrslqsJ3Ba76rvPnNaWLdIju9KH2OSFvHJz2RGybSgub96AL4nsXw7d3dd/cdua3t8C2HX73CTxQA4f/ruee6yBDEcEKCgq7xOsKoDd17Qc6Ldscu71/4HTw7TE2/Cwyv/ye9nnQO9YJu7iOI8EBCg5LQ6WWErpqmNqIYLPVGpfjabAkdTinwtHi1vC8N2eyaf48NDo9cijEtuWJjH16Dntj/S7F+s7kDB6KMSmZvGGHUbxyKfbGBixp6eQOPQyd0Rh9Pw8mHLYmtiyaz4jjTuKivz7G0RdfznFXXMdJ194MgM5o4oizzmfqZVf37DxqW9D4XdTcdQcAuYPEZ8ugFb7eKvM+SIdwN8Prp8Kn1yXeHxHHuWNh8Ysiegki3cDTAkPDplMNRVC6EFoq4Yjroe/Ezp/bnAFyEDyJU4/2CJ8DtGZRWDbkdLjwDRhwLGz6puPivkTYSmHrHJELHWh/R6hbWPYqFEyEwSeDtmsXpr0FRQQrKCi0IxgI8e3Laynb2Cgiwaau5WJac8zIodgPQCjUC5Qw4HE649blUIjZL/8LEPm8AAF/kB3rG6NRY4/Dh97fSp9+yVRuSZw6Mv2hJ5h0wSXtooS7yo1tK54B0vLySemTzaYFP+/Wa9obvC4XtduLExYEdsaoE0/F3lBP0ZIFmFJSUWs09BkwmPod23tgpj3L5oW/EAoGmHjexWh1etQaDePPPJeUrD77dB72igZ0vtiFVs4gEQn2esRnUZubk/C4bsUdLkyrWJF4v9cuor3H3Q+tlbDhc7F9+Wti+9G3ifWvboM3zwBvCyR3scWzMXw3xt2NKVo+p3CekCT43Ycw8nzhSWzbDnWbun6e0gWx5Y1fdt/8IjQWQ8MWkfvbHS2qDzEUEaygoNCO5loXpWsb+Pr5NdSW2rsugrPjC6EqNx/6ecEAPpcQwX6fF1drC1VFmwn6/Rx5zoX0GTAIgJWzdvDNi2so39iELMt47F68339Dfl8tdTvsOJvbt53OP2wkky++PLYebpXcGafedAdJ4RxTW1UlY046nfINa6kq2o0f572gcssG5FCI/J26xHWFaCMIWcaUIsSaOdWKq6Ubo3j7CGezDY1WhzW7i2Kth3A0ONH7WpG0WoJ2O2kpIi1gkHormtwcVF3M3d4rnOGcXKkD2eG1gz5JRCvVevj8Blj4vLiVP/xsKDgStCYhkCMM7GJjj0hrYF83uqRERHBbhoQb6hb/JB6bSmDLrF1Hhh3h/mPpg0WhnS9xjcEeUb8FXhgnljM69pbuzSgiWEGhFxIKyXz4yDJWfV+WcL+9STQ6sFj1+NyBaLe4zrDmxP8oRDpUHep4wiL4f488yCvXX0bR0oWoNRqOPPfi6Jj6MnG7d9vKOqqLW5BRYXZVk+naBsD2tQ3Me2cTlVtstDa6+eixZTRWxv9oX/TXx7j93c87nc/I407i6mdfASApPYMRx54I0K4bW09RvmEdKrWG3CHDOh+8ExZretRT2ZyaGn10HoQi2O9x77JZSHcgyzIB264vNt1uGZ2vBdnvp+iIIyk9dgo3vXwcfat/RVfYeTvvbiFSmNZRNNJrB32y2G8KpxDN/atIhZh0s9g++Q4463m4fQ1c831iV4hE6MMieOeCuz0lFISAOyauIyTnQvogWDYDXj0Bnh8LH1yy62I5Z4NIqzj7eZHvvPDf3TNHgNXvx5bTB3XfeQ8hFBGsoNALKV3bQGOlg0WfbUu4394oRPCF907ggnvHM268nur/e4jiM86kYearhDpwRMjsG8tVzRmYEvUiPRQJ+GL5e16nE1mWo5HW1XO+oXDMOPQmERmXZZnaYnE7uGpjHVsWV6MmQEbDWkJfv0tyuoEtS6rZuLCaL/61ijU/ltNQ7mDJlyVxz6lSq9Fou5afrdUbuPj/HueCBx7BmJyCWqvF3tiwR6/V7/MmLP7riMpNG8gZPCQuj7mrqNRqzOEIsCn6aCXg9eJLkHvdE9hqqmiqqmDOf/7Nz28n8K3tIj63G10Pi+DWb79j65RjaPnqq4T7g14vXgwkFWRCmzQa97Ll+EpL0fXr16Pzi+LqLBLcKiLBEJ+3mjdeRIEBjrsXxl8J1n5dywWOoAuft7ELF4HF86Bs6a7H+MLpTztHgkE4LzSXCUu2SAqHrbTjcznrRc5y4dFw2Dmw5GUhsruDlnAR4bBp0GdE95zzEEMRwQoKvZCGchGVNFhigsrj8BMKd3uzN3pQaSRMyToyLD5qLpxG88cf46+spP7ZZ2l4+eWE59Xq1BQMt6LSSKTnWWiqcuyWeDqYaKmLdTHzupx8869YlXgoGGTIxMnRdVuNC49bxuBuoKU5yMaF1WR7tqEJevFt3ER+HtSUxNwbti4X526q2ruLiIIRo0nOyESSJJLSMjpsOtFUVRHXUKNoyQKeu/w8fB43rQ11PP/7C/jiqYdpDbtNJEKWZTxOB3IoREP5Dvr03/PIk98rUkMsacK+yRx22HA175to8Hv3/4k3/nQj6+fNZd1P3+/xZ9jn6bpDxp7iryiHYJCqe+/D9vHH7fbbV28gpNKSMrSQvjNnRLc3zpxBqLV1H0aCIxdgu4oEh8VqWxEYiQLvDZFI8Ne3w45FHY8L+OCdc+H1Tjy1dyWCj38ArpkDf/wNjrtPbGspbz8ugqtBdLQDUWDnbe0+T+PWKiicDJe8B9p9kPJyEKKIYAWFXojbITxqPQ4/8z8qomRVPa/9eT4/vLERZ7OX7WsbSM0yIakkXMtjt/KSz5qGcexYXKvaV+q7167FW7KdabeO4YZ/HUt6nhmfJxhNrTjUaK6NdXpb8dWnFC1dyMjjY56hA8fHIlWVm4QA6Ncaey/7lC/EdNQkALKlNk0AEK2qLWl6/N5uiggBWoOBLYvns/Dj9+K2b/z1J96862a+n/l8dNuiT94n6PfzwpUXseDDdwAo+W053zz/VHRM/Y7tBPz+6Pri/73PS9dcwpfPPIbf6yEtL3GTkK4w9vSzyOo3kGFHTwViEeF9kRIhyzLecKHjmFPOxOd2Rf2Wd5d9EQkONrcgGQyYpx5Dzf89hGtFrPBMDgQouedvAKQOK8R89NEMXbmCtCuvwLloMcC+iQQvfy3WTS3YgQOCpyWWXiCHrcyGTROR1b2lbdrC2vCFQigIDTvdCSv+Mba8q+htVAQncNWQJOg7CSyZQiQbrcLJAkTL4v9dA542doXO+pgIzhsvHitXdvqSukRrZdeLB3spighWUOiFuO1CvJhT9ayfX8msGesA2Lqijg8eXoqjycMx00UhRaAplm+oSU3FMGIEnk2bkINB5FCI2qeepvbppym9eDolZ5yBSq1CrVWRlid+IJoqnXRGxRZbQpuwA5nmmurocn1ZKYWjx3LKDX/kD6+8xfUvvh7X7KJidSU6bzPjrjkuui2lchXmI49ElZJCctVadMb44sOB47LwubtPBLsdIvq/5NMP8IZzmDcv+pVZLz2LRqujeMUyPE4RDTa3acu8af686LLHISL79sYG3r7nj/zyzmvRfeUbxGeoeIW4ldxRp7yuMPniy/n9k/9GF+44F3G82FMxujtEBPBxV1zPwHFHANBUXRk3ZtvyJXz1zOP4fe2LGduyO17Je0qwuRm11UreM8+CShUVtwCeTZtwe8XPfFJ+BgAqs5nU6dOjY/ZJJPjbO4l6Jrpt7W/3yzI0lkBauHV0RARP+VP3dF/TtxGrm76GYAA2fwsvThDFYxHWfhRbrlkfWw74YN4/YuI1UmCXKBK8M8n5sOI1ePdCeO1k0bnt53/A/Gfg4yugvijWsCJ9kMiL7g4RHAqBvVoRwZ2giGAFhV6Ix+kjZ1AKVz0xmWMvHRq3z5ptYvqDR5I/VNyCDjaJXNbM228j/cYb0Q8dguxy4a+uxrN2LU2vv07Ta6+3e470sAhu7OSWvr3Jw5f/WsVnT6/E4/DvcuyBQjDgp3TNb3GtjU+76Q4klQpLWjrJmVnR7bIsU73DRWrzNsyjR3LVE5O59J6RFLzyEtbLLsM4ciSe31ZQODIdjU7F4ScVcPLVwzGYNAQDIYLB7jH4n3bbPVF3iaYqkSu4ft5crDl5nPHHPxMKBvjgr3fjdtiRdhIeER9bW1UFc199kbrSYgAqNsWEQmNlOSOPP4XJF1+OISmZzMIB3TJvIBpNbZsT7LA1YW/asxznXeFqFdFmU3IyqTlCQDRXV8WNWTX7a7YuW8S8N2a0O74tu9M6ek8JtrSgTklBbTGjHzQI9/p10X2Bujp8euHVbEqJFbfqBwwg/YYb0PXrhy5/zy9WukQ7wRtsn5JQtkRYnmUND48Jf+ZN3dTNrG3E1tUAOxaIVAFkKJottntahJPDcNGcBWeb1J+tc+CXJ2DO/WJ9V+kQOzP0dPG4bW74mCSR9/vjw1C9BoacChOuEftUKlHs1x0iuKVcRN1T++79uQ5hFBGsoNALcdv9GMwiHzhnYMxXdtTx+Zz35/HRFsgAAVsT6tRUMm66CbXFgraP8DgN1NVj/+GHducONDbS9PbbSA3VWNL0NHYSCW5bnDf/46K9el37Ap/HzccPP0Dpmt8wJE9i7Bn3cNOr70XzV3emqdqJ26sizVOOtm9fzKl60gZkkXT88aiTkzFPmYKvuJgjp1g4+VQTfWbcQuj602h9500A/J7uiQbnDTuMk66/BRC2acFAgKotmygcPTaautBUWc6a77/DaWvCmJyCRq/HbE1j3BnnMHCCSO9Y9+OcqMuERieEldveiru1hfS8fCZdcAk3z3w3LhK+t0Qiwj53TAR//59/89Uzj+/1uf0+L7Xbi6PrESs2Y0oqKZl9UKnV2GpiItjv9VCxaQPmVCvrfvqejW0i5REiOcR+t2vfRILDkfvIXZoIgbo6vDrx/Tan6OOOy7rzTwycPQupi4WWe0zbfNiCScLmbMNnsW2hELwRthbLChdvjQ3bApq7qZV024s6rRk2fCFybwG2zgXbDvj6Dgh44KhbxXZHGxGsDl9AlM4XUeSSn8V6onSInTnhAfhLBeQfAee8DFPDneFO+ptwuZj+TiwNAsRy7Xrwd5BG1lwmWkt3Rvky8Zh/ROdjezGKCFZQ6IW4HX6MSeIPe0pWTPCOOCY32so3QrDJhjot1vVMkyl+mAL19djn/oB58mTMx04l6dRTAXAuWkzt4/9g+3nnR4vjOqKyyMa2FXUccWY/jjyrP0XLatm2suPiqwOBkt+WU7VlI0eccx1e7xg2LQ6gMyT+MXS2ePnwYfFj1CdblbD1tOWYKQCEVizAe9eV+MvLMR01CblaiAefO9Btc0/tk42kUtFUVUlLXS1+r4fsgYOjnsIAWr0eh62JIZOmcP59f2Pabfeg1miYeF7M7m19uFNezbYiVnz9GSW/iVzn7IEihSZicdZdRCPB7pgrib2xgZrirbj2IkXC63Iy48YrePe+27GFUx4i5zMlp6BSq0nJyo6LBDtsTYSCAaZccgX5w0cy99UXaayICb2ZN1/Nu/fdQWtDnSiM6+mc4HAkGEDXt4BgfQMhj4dAYyPe4hJ8+lQ0OhVaQzekFewJ9W0ubI1W4aW78atY8Vdj+CLYlB4Tg8fdD3+pjE9j6C6GnCJSITzhz03ZYtGyeMNnMP5q0VnNmBYvgiPd7GylMPNY+PUpyB4tusV1BX0SXPcDjL0MJt4Ipz8FE29KPDZvPIQCHQvdmcfDf6bAR5cLQd4R5UuESM9SXCF2hSKCFRR6GXJIxuPwR50h2opeU1J7P+CgzYY6zRpd12SJW/3OxYvw7dhB0skn0XfGDLL/JgpfHL/8AkDI4SAt24itxpXwln4oGGL+R1uxpOkZe2oh404rJKswiQW7EQ2WfT7kYPflzXaFiFhS62NpJB0V/9mqY1HwzMMKEo7RDRyIJieHli9i3aLynnwSfYq4OOlKcVzAZsO1svNbqGqNltQ+2diqKvA4RCTMlJKKVheLEjZWluNx2LFm51Jw2CjyDxMpFDmDhnLrGx+jUmtwtLFaW/zpB2z89UesOXnkDe+ZH1yNTo8kqeIiwW6HHWSZsr1op7xm7qxoDnAkRcQRTv+JFONZc3Kp2CyafwC4W8X7Zk61cuZtd6PV6fnl3VhutL2xnrrSYl6/4w/7qDAuFgnWhlMb/BUVlP7ud9jefRdvcjZJaYaEF2D7hIbw97lwCpz2uOis5moQ+bjF82IeulfPAk34749K1TMCGGDAcSLVoSr8uQkFRKrCqIvhrOdEYZulT3w6REQEj7wA6jaKSPUffgVj6s5n7xytESb+oWO3hsiFQHmCNueyHHPZ2PQ1rHm//ZgIZUshfwKou9boqLeiiGAFhV5GS70bOSSTmtX+x1lvbn9rNFBfj8YaE8Hq8HLzh6KIxHLCCQBorFY0WVk4fv01OjbV6CMUlGmuae8rvGF+FY2VDqZcOBitTo1arWLguCycLT58no4jHAFfkNf+PJ+iJZWsnX4Dxff+rWsvvJvYtGATWmMqmxbWo9GKP6Et9UKc2Zs8cXZaEXE8YeWTGEcmFoiSJGGZMgXPOhH5yX/xBdSpqZgKRT6qr5N0iEBjIzsuu5wdV15FyNl5EaI1J4+m6krcdiHmjBZhS2UMF59tXiAuYgYdMandsXqTiYIRogvc+DPP4ey77sfndlO2fi25Q4b3mNCSJAmd0RiNBIuOe2L+O/ZCBNfv2I4mfAHQUleLLMus/WEW1py8qFdxekEhrpZmln8tbuFH37ekZCxp6RSMHBMtkgwGYp9bKeyHa0xK3uP5dUbI6yXY3IwmPZ1QMESpI4OgSkvL19/g3yEa4TjN2Vizu5C72lM0FIko79XfQtoAGHRSbF/ZYiGC9SmiY1pPctbzcOmH0Pdosb5jAVj7x/YPPS22bMmE1ljha1QET3sO/rQBpv2751oQJ+eI9+LHR+CLW2DlW7DoRfjpsXCBIWBIAXOWKNbzu2HxS/D2OeAKt6b2tELdBpF+orBLFBGsoNDLiDSwyMiPNbY4986xTDizX7tUCOeiRfi2b8d0RCyvrO2tbtOkSWizYkVg+qFDCbXG7H+SAsIgf+fiOI/Dz9KvSsgbamXA2NiteHOqECSJWghHxKW9yYPH4Wfum1tYlHEJq7an4K+qaje+J5BDMs211QT9Sfi9QU6/UQjCb19ay7cvreHt+xdF0zlWzi5l+Rfilq/FUYVhZMctj81TpkSXNX2yAdCnikiYfxcXBCGPh7JrrsVXUgKBQFw+qK+0NGFTE2tuPg1lpXzx9KMAGMIi7YaX38SUkorf66HPgEGkZPVJ+JyR3ODU7DwGjDsyGjHdG0u0rqAzmqKR4IDXGxWcO9au3mMf38bKcvKHj0Cj1VG+YR32hnoaK8oYe9q06Od80vnCSSGSNxwVwcnifTOnpuJstiHLMr++GysQPeOPd3HRXx+Ls83rbrzbtkEohH7IYEjj4SUAACAASURBVNbOq2DBL27KCk6kcYYo2AtJKlzqFFKze7Y4b5c0FEFGm+JbrREuDL9PFSvEv/zxIvrbk4y/UhSpZQyGAcfH5nLEdUIsHnZubGzBRKhcAaULxHrUDcIi3BZ6Orr6+89g1EWw8Qv4+jb4/gH49WlY/5l4L6+fBxe9AfYq+PcYWPeJyFN+6yxY8yGs+1gUF0aajCh0iBInV1DoZTSU21GpJNLatDjOG2Ilb4g1bpwcClH3z2fQ5uaSesklcftyHnsMSacj6dR4U3nDsKE458+PrdvKUanyRHFcm/qMpV+V4PMEOebiwUiSRMvX3+CYNw9HbQiSzqLhl+UYD8/BMFT8eC7+opjNi6o5+47DsZfGPHVDah1uQwa2Dz4g66679vq96QxHsxc5ZEOlHczI0XrkFx8CzgegdJ0Q/JVFzQwYm8mSL0S3N523BZUciN6qToT5qEmgVkMwiCZLXBToU0xQB15nxzZczoUL8W7ZQp+/PkjtI49inzsXORDEMGwoxaedjmQwMHT5srjip7TcPLEQFo7GJHExpNFqMVvTcLU0M7hNo4+dGTppCkWLF1A4agxqjYbDpp7Aiq8/Iz0/cbpHd6EzGvF5hKh3h1M5sgcOpqZ4K01VFaTn7d7zh0JBbJUV9B0xGqetiW3LF0ebgURymwF0BiP9x06gsXwHCz58m6IlQhhFIrzmFCs+t4tlX/6P32aJrm2Tzp/O4COP3rsXvBOyLEMohKQWub2ulStpeOU/AEiFg/nt9R0ANGWOov+O2ahMJuyqNGRUWPe3CB42LX7byAtg6w+w+RshMIfdve/mI0kw7goomSfyfs98RnwX2kZ2j7lLtC8umgP9pohIsC6p54V6hNS+cO5LcMbTwkfYmNr++dMHwsAToPgncNRC5jBoLIbP/xB+nSqlKK4LKJFgBYVeRkOFA2uOCbV211//xlf/i2fjRjJvvw2VLj5XOPWC80k5a1q77fohsYiPymIhsG0LqdkmmipjkeDmOhcb5lcy6tg80vMstM6dS9Xdd+OYPx+9TxSrlL/8BlV334Msy4RCMr/N3oGr1cd3r6yjfrMQwVIoQEGhFo+lD46ff96bt6TL1JXWg+zBmptH7ryXcc37kdF1X3FM/bucNbocg7+Z+qJa6nfYo8fo/Hb6vv3WLlMF1MnJGA8/HCQJTbpwmdBbRSTYa+u4sNC5eAmSwUDqRRehzc2l6a23KbvqKirvFBcEssdDydnnEGqTS5uWEy/GdW0svCKpEUMmdizgTCmpTP/bE1hzhJged/rZDJt8bNR+rafQGYzRSLDbLt7foeFmGm/eeRPPTJ/Gj6//h1AXc8Rb6+oI+H2k5/fltFvuZPSJp1G3owRJUpHRN94715qTR2NFGUs//xhbdRVqjQZt2PUh0s1uwQdvRccPGNd9ETj3uvV4S0poePElik85VfhzB4NU/eV+nAsWIOn1bCyScdv9ZBUm4TSIOzO5/3wa13WPoNJIFI7sJqux3cXZKNolZw5tvy9jsHBokEP7XqxlhC9yIvm1O383tUbIGSOi1BBu6dxDOcq7QmcCa6FIf0gkwM9v09J76Olw2ypIDl/kZo0AQ8+l4hwqKCJYQeEgYfOSal668ae99tJtKLfHpUIkwl9bS/2//oVh1CiSp03b5di2GEaIvNeMm2/CfMwU7D/NIyXDQGtjrHCstqQFWYYRU/PwbN5M1b33YRgzmsEL5jP0TdGO2au34i0qwrlwUdQdYdCE/2fvvMPbqLI+/I66rOIit7jb6b33QCCBBAgEEnqHj95hKcsuy7KwS19g6bABEjosvZNAEkjv1amOE/fei7o03x9Xluy4xHacAsz7PHksje7cmZHl6My5v/M7sTRUO1m3TnScOndSOUmjUvBIehrzS5C9Peei0BZluXX8NG8+ACNP6I176yZxrTsXot2xmsYXnsBWtpXqMif56w4E90s/8B3G4cMPOX/UpZdgPfNMJI1YoAuLFl9g6xYWUp7btgOCY/s2DMOGo9LpiPvb39CmpmAYOpTGVSEfVveBA9T9uDD4PLIpExygeXAek5pO0sAhwQC3M1hs0cy6/d4WnslHAl1YWFAT7AwEwfEZLXWkWxZ+yxu3XcvWn3445HyVhUIza0tKJjYtg1Ovv5Vz//oIp95wK1p9y6KltGEjMUdG0X/iCYDQ/ja9b01BMED6CFHU1CQROVwc27aRc/755F5+BZXz5+MpLMSZmUn9kiV48sT5e2KS2bK4gD5jYkkfHo1XE4Y6LYMifT92bLGTPCAKo7l1wetRoakoLrpf69eab2tuEXY06IyrQ8oEoVeuyQdXQ6il8/GEqdnNTUSK0BMPPU88V6QQnUIJghUUfiNsWii+9Mry6g4xsn3sdW4aa91EJ3ec1fAUiEr5mNtvDy6/dgZ9RjoZP3xP9G23YZ15Gr7KSlSNNXjdoexcZVEjKo2EWe0g/+abUVssJL34Iiq9Hq3JgNZTj9MgLNmq3noLZ6MI+uMopvcI0fVK7XUQe/lFWGwiWHGqrbjz8zmSbF6UQ2OVsDuzlBWBLBN9yy3B13s9/jh6Vw0ev4acjYWENZYwddldxFRuQ6XXtzdtEOsZZ5D4dKgtsT46AmQ/DrvMkueXtxgryzKe0lLqi+v4IewyPv/3Rqrjh9Fn4UJS338P46hRaBMTSfvkE1RmMzWffRrct6MA7aQrruWCvx++925beAoLhX65m7+n5pnggt2ZSJKKyIREevUbEBwzfs6F1FeW8/MbLyPLMj6vl92rluE/uGEDBG3NoprJKNKGjWToyTNajU0fOYYbXnuHqZdfQ0R8L6Zefk3wtSZ7ubGzz2X23Q9w3gP/aldP3VXsm8SNlq+yEtnhAEmiYcUKquYvgMDNUrllAF63n7Gz0gmzis9Zrw8+4+d3RQAal34Ms4FVQhLUZtAZFygUjRkAYVGtXz+SaI0w7CI4/+32x4y7QWSIlz0lMsGd8QQ+FqRPFT9TJoqfsYPEzz7Tj835/MZQNMEKCr8RmhJ2FQUNpAzq3vJmZbAo7hBBcJGojNYm9OryMfTpouLaPPVEJKMRf1E+Hk1oebmysJHIWCNFd92Jr6qa1Pfea1FcZ5LrsRtjCJ87l9rPP0faKXxE6996HSlpMERNxeC3I6nVwa50dZYU3Pv3B4/dk9RXViADBbvETYhKrUG1cROaXr2wXXsNFS+/jPmU6UTMOQfr11sBKKs3kkwWar+728fVREaCJDLoFfYwSrKrie8tso6Vr79O+X+ep9Y2BD8qqovtfPfyNq599kS0eh0pC+azc2kOBc5wbDdcT/kzz7JrwEBSFizANGE84XHx+L0+zr7ngVbH7WmPXwBPSQn7pgtXgJKH/kHKW28eYo/WGCyWYFFa9vo1JA0agikiknPufZDKgjxi03qjDwtDUkms+ewjXI2N7FqxlCXzXyfM+igpQ1pm46sK8zFHRmEwdT64sdiiueb5eS22RSencsVTLxKdkoYkSaQOG9Hla2uLxrXrKHviydCxZ8zAU1godMBeL7brrqVy3huo+g+BGgiPNlIXcCmpbCY/ik4+hhnMJlcFQxs3XlHpcPPaUKvko83cjrv9EZEsfIPXBcb1nnbkz6k7nDcf/B6wiIJahl0obiwSeuZz+HtHyQQrKBzH5O2oZN03+8nZXkF9QFJQkt295gAbvs9h2cciO9SWHMJdUIjfKY7hCVg+aePju3UsAJXRiOXkk/DnZgdtvtxOL0VZ1Vgqs3Bs3EjCY4+2sg6LGd0fd0w6cffdixQWRtlHwprK3CcZvVNYABm1IjscGReG3qim1pqBK3t/t8+1PTxuFx899Ge+fPJR7LXi2Ofc/Vccq1ZjOflkVEYjfZYsJvEpkcG1JIaWxhMHRpP8xhskvfxSt47dvEEJwMaPRYDtKS2l4vX/AmA3iizkpHN74/fJweDH7ZH49YsCFr25A9XU04NzVL//PgDXvvAG178yn7iMPt06t67SsDTUVa1x1Soa167r8hzmyCjsdbX4vF4cDfWEx4rPZpg1nORBQ9GHCW1zTKoIql6+5iI2//gNADWlxa3mqyzIIyqpZ1rKxqSm96g9nKe4mMI77gg+Dxs/npg7bsc0ZTJ4vSBJ2G64kd4//oB2zETUGhVqrQqjVcgeCvdUAzB8ejJpQ4+RHhjAE7Ds07ZTmBc7ADSHXiU5ZpwQKLbVGGDm48f2XNrDZAsFwCCyJUoA3GmUIFhB4Thm3bcHWP9dDt+9vC3YNCFnewX1Vc4OvXQPpqbUztqv91NTasccqQ82ymhC9vvJPuUUCm67HQBvcTHq8HBUpsPTeVpOOw1VYy0+jx+/z0/u9kq8bj9Raz7GdtONWM84o9U+kWnR2L06Nq6oxnb11dTtFMFt9GnTSJuUTnhtNiNsYilbUknEpllpjEzHnb0Pd0EhjatXH9Y5N2fjN19QV15KeW4WPm8OAKrMncgOR9AfWZuQgCoQgEWNGxrcN/200ZinTMYyvXvLkppm2fHoyu3k5PlorHFR/tx/wOsl/p+P4AiLQ2dQBZ09muzv7HWhDPT37+ajnyS0rN7qqm6dy+HiragESaLfhg1oevWi9MknutzkxBxpA1nGXluD1+VC247ExBodet+qA53efvrvS6z83/vB7bIsU1mQf8QdLbqD7PFQ+Ke7kd3id6hLTSX17QXoMzKIuuIKzNOnE/fAA6jNJnRpabhdfnRGIVkKCwTBBYEgeMQpKceuSQYID1tJdXwHuh1hiRPZ6rt2iIBd4XeHEgQrKBzHOBs8pA+PDj5Pzf0B2Q/v/HUV8+5c1sGeLWn6UoS2pRByIAPcZG/m2peNNjW11biuYpo0CbUssrYet5/CrBo0eIiSqoi+qe22of3Hi6zGnjXF2G65GU8gi2Qd0g9zQgyjNz+LRRVyX7BEGXAZIqn74UeyTzmFvKv/D09x68xfZ/C4nKz46F2cDQ3UV1aw9qtPsMZmABp8TpG9dL7/IYZhw4St2UHEnCSKUWL0NUSOPLwvzebBS/9kFyBRtH4ftV99RcSll+GfdDqq6bOJiDNhsRkwmLQUZdUg+2UcgSDYYNJSW+6g9sK/EnHhhbh27uq2p+7h4K2sQB0RgdpsIvZPf8K1cxe1X33dpTlMkSIz3lBdidftRqNru9iruR7XHBnKpq/57EO8Hg+NNdUs+NNNeFxObIk9kwnuScqe+w+OzZvp9a9/kv75Z6S8E9KtaqKiSH75JaIuuzS4ze3wojMGiikDHR8r8hvQGTWYIo5RQVwTbrvIAh/LQPxwiR0ApuhDj1P4TaIEwQoKxzHORg/miFAWJbJmH/FxoS+UttoRt0VDdcidwRTROivTvKmC7Pfj3LGj3Q5nXUFtNmOMF18gHqePoqwaIupzsEw9oZW9WhPWaCNTL+5HXYWT8rwGCvrNBsA8oDemSaL4wzojVLxkitDjkgxo0jKwzJwJgLeiovXEQN3331Nw2+34Gtq2HcvesJa1X3zMtsU/suz9+eK9cJ6MPvxqYtJn0LekCn9eHtaZM9osGNRbTVzzzAmc/585PZKBG5lWg8FbR/KZwre35Oc1IMsUJp7AR/9cR/6uasJjjEiSRMaoGLI2lPLa7b+wf0s5AOfcPZKkAZFs/jkfdVpv/HY7vqqjnw32VVahiRbL8tYzZ2EYPozy557rVIe7JpoC2obKSrwed7DT28EYLVYu+PtjpA4byaQLLmvxWlVhPlnrVgdbJCcOGNSdyzli1C9eTNVbbxF5ycVYzzgDw6BBaOM6LrJzO7zoA0GwWqtCHyYeR/UyHdssMAg5RHtSCAWF4wAlCFZQOE7x+2VcDi/6ZtIFnauW1JiQ52tjdfuNFJrTfFx4bOsvpeY+stXvvou/sRHDkKGtxnUHfYwoiqnanUd1cSPW8p3o+7VhmdSMPmPiMJi0LJyXiUM2EJ9mRq3ToO/ThwGZ2zGfeGJwrDnSAEjEzv+AqKuuBMBXU9PmvGXPP0/9Tz9RtaDtqvCcbZsB2Pjdl+xe+Stjz5qLSh2OpLKQ3m8SfUtFRr3JCq4tDCZtjwUfk+6fyzVvnINlYAYar52qrBLQaCiqCgWA1hjhVztiejJ9x8RhNGnZtlQEeWEWHWPPTMdR72G/S/gDuwPWWkeDJsmDt7ISdZQIgiVJIu7++/GWl1P55lsd7d4Cc2D/Jn1ve5lggOTBwzjvgX8ydNoMrnr2Vc64XTRj2Ld+ddBmDcDWQ5rgnsCdn0/R/X/BMHgwsfff3/n9mmWCISSJiEo4hq2SAQo3wcYFwutWQeE4RQmCFRSOU1x2D8giqGpC764l1VgalEg099/tiPpqF7FpVqZdMYBh01p3LmueCS59/AlMkyZimdHaKqo76MOF/GL7w6KYK6JmH/o+HRdkGUxaxp+dESwGPPW6UEDe5KPbRKjVsls4KgC+6uoWY/x2O7Xffhd0vXAErKeaI8syWes2IKm02GtrMEfZGD3rXABUaon+ppC1V0/dIHQWbXw8Rmcldk04uv4DKdofkoOImwCIjDcx45rBTD5feOdKKgmDSUtCnwiSBkSyY6+ET6UL+sseabwVFewZM5bsWWfi2rs32AQEIGzkSKxnnE7lW2/hKSnpYJYQRqsVSaUKBsHtaYIPxpaYTP+JU+jVbwCrP/2Q5R8sAODifz597DOlAfxuN4V33gVA4vP/aXeVpC1cDl/LIDj8OAmC5wVaE3N8vMcKCm2hBMEKCscpTU0xDCYtg05IAEDjtSNXVzL5PBHo1FU42t2/ibLcOgr3VGOJMjBwUgJqdes/++ZBcK/HHiP5zTdRm3vmS1QfKZwoctJOR4UPq6MQw6BDL0MPmpKALdGMKUKPJcrQ7rim16pLGlFHiKxz80xw6ZNPsXfyFIruuQdNdDRh48bh2LatVWFWVWEBbnsNav14zFFxTLv6Bjxu8V5Nvbg/vp1bUVksDNi5o8fem84iSRJmtR27MRb7gIl4XT7iM8IBMEe2DAb7jI4lsV8E5kg9kkoEIGNnpeG0+ymPHoY7LxTMu/PyjphG2LVvH7LDgaTR4Hc6W2X/Y+++G/x+ih96iKxp07Bv3tzmPI2rV+PafwCVSo0pIpKagHOJppNBMIBKpeaih5/k1OtvDW5L6DewG1d1ZCh74gmcO3aQ8MTj6Dpor90WHufBmWDxvhzTINjZzMHGc+j/oxQUjhVKEKygcJzibBTuDwazlrED7Jy8+UEkwJ1fEMz2OOoP7UO7e7XItDUvsGuO68ABHFuF/Vbq++8RMbdn9KxN6KNDHqEqr4uo8+agiYk55H4qlcSZtw7nrNuHd3g+kb3CMEfp2b+5HJVVNAYofexxfA2NyG43Ve+8g3HYMFLffYc+i3+m4cTJrIqzUr9mbYt5dixbDEiodQMZf/JN9B03icYaISMJC9fh2LYN49ChR8RHtzNEJIbjNERRYeoLEsy6eRiz7xjRqiWuJEmcftMwzr4zZJMUmyreF3dsGu78QKexoiKyZ8yk9J//OuSxPSUlyP7O6c+D+xSLz13Siy8wYNNGbDdc3+J1bWIiUVddReOvy/AWFVP19jut5vA7HOTffAuVrwuvVnNkVDM5RNccB1QqNcOmn0Z4XDxDTj61S/v2FL6G1hrouh9/pPqDD4m6+uouOYk46t3UlNlx2b3oDc2C4EBxXFSvYxgEO5rJkZQgWOE4RgmCFRSOQ8rz6vnyWbFk79+6nvxLL0UK+NTWL1qEe9tmVGoJt+PQNlPlefX06hMedF0AkaUrffwJKl57nQOzzw6a8jdZffUkGmvIjSKmKrNdV4i2MEfqsSV03MxAkiT6j48nd0clFYWhIKPixRdFdzKfj4i5cwgbOxZJpWLRku+oMhvZ+8xTQX2sy25n66LvUWn7Iqks1O3JBaCqWMxnCgPX3r0Yhg/r9Ln3NAmzTgRJxc68MGJTLBjMWpIHRrV5g6A3agiPCf0uNTo1RosWd0Qinrx83Lm57JsmAq7qDz7AsX17u8f1lJSw76ST2T1oMLXfftfp8/UE7Mk08fFIOl2b52m7/jrUAZmENi621esNK1YgOxzBQkZTpI268jIxvotBcBPXPD+PmTfeceiBPYhz507yrr2OvWPG0LA81P2vbuEiCu+8C21CArF3/6lLc/7y/h4+fWIDHpcPc1Toveg9KoahJyUFtcHHBFezrpYee/vjFBSOMUoQrKBwHLLy0yz8PrFM7Xz278HtSS+/hDYxkeIHHkBnUON2dOwV7PfLVBQ2tOoaVfXee1S9/Tbl//kPuoyM4HaV0diDVyGIHZpCcv4SRmx5gYknmlp0h+sJfn3vLarzv8EQpmXlp/toWtyvevtt9s86EwBdoJNcXYUdf0AGUVpfw4E5c9nw3zd46eoLcDvsaAxjALCXikzW7lXFRMSFEVZ1AHw+jMOOXRAclRRqf9vkC9wVLFEGnEYb7rw86hcvCW5XWSzkXnIppY+37d3buHJl8HHRPfd0yt/XnZ9PxQsvivk70LeqzWaSXnwBCHgJH0T9op8A8NvFzUhzy7OOCuM64ljogIv++gDOzEyQJBxbtlK/ZCn1ixcHG2LoBw5spXXvCJ/XT/6uKlx28fdva2Z72KtPBCde1K9717nnR3h3DjiqDz22I1whzTp+z+HNpaBwBDlkECxJUrgkST9IkrRIkqQvJElq838eSZLelCRptSRJf+v501RQ+OPg8/mDGcgh0hZUahVh48cDYJo8mV6PPoonLx9VQzWuQwTBtWV2vC4fMckts6m+WqHZS3rlFVLfDzURkI5AJtiQksSsj+9nwqJ3iL/j1kPvcAhkv58lC16nIj8X2e9nwzefs3P5zwybFkne9rX8MLw38e++TdyD4r+iOksqhY0RlOfVs+jNVcF5CtKT8Njt/Lr4SwDCzJGoNCJb7qh1UFXUSHF2LYMmJ+DcJjKlxuHDOVbEpFg446ahjJ+dzohTu+5qYIky4NRY8VVVUbfwx+D29M8+RfZ4qHr7bRxbt7Xar2HFCgDUgaJDT2Eh7rw87Jva1vACwYYlnVlZCBs1CuPo0XjLylps97vdwU5zTZr1FkFwFzTBxxpPQQHWs85Cm5iIY/MmCm6/nYJbuva34HZ6g/8vlGTXBpvnAEQndr71c7v4vPDxpZC9BL4Xbhpkft4yoO2IH+4XQTSAs67jsQoKxwmdyQRfCjwry/IMoAQ47eABkiTNBdSyLE8EMiRJ6tuzp6mg8MchZ1sFjnoPZ9w0lKT8JZjGjSVl3n/pu3IFKoMB0/hxhM+Zg9pee8hMcHm++AKLSWmZCfbk5WOaMgXLtJNbFHmpwo6MjlBtNqO2WnskC1dXUcbmH77hwwfvoSwn1Co5zFKO370eAGdEOFGXXkrfVSvJnPxnFr69j/89tp6iPVkARKedTkNtDet7i4JDSR0H8szATDJOF+xYlo9KLZFqKqPq/ffQJiWhOaiV8dFEkiTSh8cw5oz0bi11m6MM2H169MOH4QwEu7H33I0uJYWMb0TjCveBA+JnTg6lTz2Nff167KtWEz5nDonPPgMIrW/2jJnkXnJJu8dyZ2cD0LtZsN0RmpgYvOXlLbbZV6/G39CAymoN+gmbmr3/3ZVDHG38jY34GxrQxMagy0incdVq8HqJ+9vfiLz8csLGjSPmjtsPOc+6bw7w4cNr2bO2hLydLb2ejZbDlD74/bDoAfB7ITIdtn8Cix6ET6+GH//S9j71pbDwAaH59Thh7avw5Y3QUN4ycFZ8ghWOYw4ZBMuy/Iosyz8FnsYAZW0MOwn4X+DxImBKj5ydgsIfkMxfCzFH6kkdGo2vohK1zYak07WwmNKlp6FxN+Jq7LgwriKvAZVGwliZy/65c3Hn5+MpK8O5Ywe6lNbZRJWxfReG44WGQLMHt8PB4jdfbba9ApVGZMd8nsASrDmcuoovMVtWMuOaAST28yBJalzOgfSfeAJVJgOSrEZnuYA4bx1DB8j0ivbh0kewZ3URiaZaSq+5HG9R8TGVQvQEligDXrefmMefRR0djeXUU7Bdey0AuowMJK0W9wFxU1H1/gdUvfUWuZdfga+2FtPkyWh79QLAvT87OGd7fsyurH0YBg/uVAEkgCY2BndODvvPmUP+jTdR9Of7qZz3BpJej3nKlGaZ4NDfQFcL444VTcG9NjYWY8BaT9LpiLz0EuIf+Cup77yN4RC+2QD5u8TnfvOiXPJ2VgatE1WqHpB35K6Ata+Jx+e9BYljYJWQqVBX1PY+n1wJq1+C/b9AXaHY5qiGH+8HV8Ad4vSn4Yblbe+voHAc0GkRkiRJE4FIWZbXtPGyCQj8FVAFjGpj/+uB6wFS2vjyVVBQgJpSOwW7qxk/Ox3J58VXU4PG1trVQRMdg8a7n5pSOz6vn30by/B5/AyaktBiXHl+PbYEM/Zfl+LauYuSR/6Jv6EBSavFcuopwXGSwYDsdLbZBe14oGBXJj/Ne5mxZ80NLoOHx8VTnL2X3mMmULgrk/rKCiREZtxRL5Zj6yob8HuyqcjLZv3XtXhdLsLjUnG6YPjMi7Fv3UqDfTAmTR1zFtyIpFKx+KVVFFdowAW2dQuC56DpYS3z0abJSs6psZLx5RdI2pD/tKRWo0tLw7VfZII5yDbNNHlSUC9e+vS/g9td+/cTNqrVf/e4c3Iwjh7d6XNrCpZdu3eDLNPwyy8A6NLSUEdE4G9sQw7RTU3w0USWZRqWCzmJJiaGsIkTqXjlFWw33tClVRGP20dNiR2dUUNloPhz+CnJbP05nyFTEw//RPctDj2OGwznvAovjxXP22t2UbBB/CzbBbrAClLaCZD5KdQHvJ9HXqY0y1A4rulUECxJUhTwInBuO0MagKaKGjNtZJhlWf4v8F+AMWPGHP3m9QoKxyFejw+X3YspXAR2mcsLUakkBk5OwBto+NDUbrY5mmgbKv8enHYfXzyzidIDIugbNCUBv19m4X8zKdlfi73OzcDJvfDuFQs4jStWgCzTba8fRgAAIABJREFU67HHME2cGJyv9/ff4crObnWctmioqmTXil8Yc+aco2YXVrh7J1WF+Sx87fngtksfew6v24XOEMZHD91HfWU5siwywLXlIkNZkS+yWEmDxlOybwtet4uh02eTtQlK9vsYN+EsflnhQyt5g9eSOCKZ3ZlCNjHmg+fQREZS/ODfibryiqNyrUeKJgeB+konMSmtM7S6jAxce/YAotGFLj09KI9oakKittnwVVdju+lGKl99DXcbQbDs8+EpLcWa0PKGDEQDGH2YttX25hnjjK++pOzf/6byjTfRxMSgMpmCmWBTsyBYf4SkOz1J3bffUvroo4C4idLGxtJv3VpU5s5reL1uHyX7avH7Zcadlc6K/4nP5oAJ8Qw7KQlzBx7anSZrEYTZ4LQnQKOHmH6gtwqXB3sbRXLO2lDBW2kmWMQqAWc8DZ9cLTLLKg1oe77QVkGhJ+lMYZwO+AT4iyzLue0M20hIAjEcyOmRs1NQ+J2z8Ydc3ntwNVVFjXjdPnavKiZ9RDS+1UtxBmyrmtrNNkcTHU2DWWSAmgLgJrLWl7J/Szn2OiGViEm24NwtghtkmbCxYwmfc06LfbQJCZhPOKFT5/zFU4+w7P35lOUe6NK1Hg6O+jo0Oj3DTz09uM1gMmOJikYfFobFFk3h7h14nCJLVlMmvrgr84Wn7OCpM7no4SdJGTKMETNOxWjRsu6bA3y3OY5GUwJ6YygDnjBQZN6HTE1En56OOiKCpBdfCMoBfqtYbCJYqq9qu8ugLiMdd34+stuNt6IcTXQ0vRctpPfPPwfHxN59N8mvvkLMrbci6fW4sve3msdbVgY+H9qDguD8XVW88aflHNha3mqfg2UT2qaGEZKEyhQGHg+y243RIhwy4vv0w9CFQPJY4dyxE4DYP/8ZXe/eAEIb34Wbx7f/soqvX9gCQL+xcVhsBhL6RhCdZMEabTx8OURNPpTthCl3wbALQtvv3CYyu3UFoW0V+yB3NVQ1+9sv3wvVgeeRaTD7BUACvQWOk458Cgrt0ZlM8DUIecMDkiQ9ACwFtLIsN3eB+BJYLklSAnA6MKHHz1RB4XdI1Z4CvG4/P87LZNhJibjsXvokOIItVKG9THA0tsodNJoSmDinN6u/EFlct9PLga0VqNRS0GJNu3cDzu3bMU+diuzxEPfg37pdoFaRl0PZAXGssgPZxKX3Dr7mdjpQqdRHZJna2VCP0Wpl7Ozz2LXiF2zJqS2uIbH/IIr27iJxwCgKd28i89ds+k+ooqZULMvGpCQSl5HI+Q8+BsDce+KoLmnkx3mZ+L0y4aNDbZCtNiMXPTiOyPjf1zKuwaRFo1VRX912EKzPyACfD3d+Pr6KSgyDBrbSjUfMnRN8rE1Oomr+fLylpfT61z+DThCe4kBb44TQTYPL7mHZR3sBWP5xFimDbag1oUDwYNu8pgBa9nqD8/oaG9FERnLDa+8Eg+HjHV9tLZpevbBdfVW353A2ioyrNcaI0aLjkofGI6l7MLjcFyj56XtQm3RjJKROhmVPQW0BhCfB/NOhsQzGXifGpJ0AhZtg07uQMklkfpPHwYn3QvmunjtHBYUjRGcK416VZTlSluWTAv8ePigARpblOkRx3BrgZFmWa9uaS0FBoSUN+eVoPQ1UFzfy64d7iYgLQ/PRi8HX1dHRGIYMabWf2majT+GPzE7ZzKiZqfTb+xEgsnz5u6paNMaQly9ECgsj/pFHSHnrTfQBz9yuUFdeRtHe3Xz+xMOYIsTSeOn+LOx1tfzw8rPkbt/C548/xE//ffEQM3UPR30dRouV8Ng4bnnzIy56+MkWr4+fcwG3vvUxp15/HwA+5zp2LN9NVVExoMaWFNdifERcGOnDY4gLdFLTW1ouKdsSzajaaC/9W0aSJCw2Aw2V7WWChV+0a+9evOXlqNvQojfHW1IKQN3337Nn1GjK/v1vnHv2UDV/PkCLzHnWhjJqSu30Gx9HfZWTbUsLWswVzAQHbmzUAdmDOiIClUnIHuRmNmnqLnjqHkt8tbWow8O7vX9zG7SmDnAanbrN1ufdYvd3sPIFiEiB6DaK80YEHEA2vi1+Ngbq4tfPg+j+0Gc6eBqhvghOuj+037QH4ML3euYcFRSOID32v7wsy9WyLP9PluWSnppTQeH3jtOrJrw2m8HpIjAZ0deJY8OG4Ospb8xD1YYfqqRSoU9JhnyRlTXZxZ/d3mUHcDu8pAwONVPwb1yB7aqr2uzI1Vnm3fp/fPjgPTgb6plz/z9IGTKMvWtW8tHf72XnsiV8+eQjlGbvI2fbZmS55yX/jrq6YPZPpVajUrVdwNdcb7p3zWLK8wrRhUWi0bUdNCUPigrMf+j2078HzFEG6quc1JTZef22X/j6+c1UFolubPo+fUCtpvTpp/E3NhI2ckSHc8XcdSeS0UjSS+LGp/KNN8m56GIalq8g8rLLkBJS+ObFrRTuraa2zI5aq+KUKweRMtjGhu8OBOU6AKrwcMLPO5fUd0SwZRgymNj77qPXIw8Hg2BvZetmGsc7hxsE11WEWg6bwg+xwrLwAfj2ro7HNEeW4aNLhJRhwFltSxciUyH9RFHs5veDxigyvrdvgVvWQsxAMS51shinoPAb4/eV6lBQ+I3h8mnRuevpU7aUKx6bhHXVJ2ji4+m7cgWJzz+PYcCAdvfVpaXhzskBwOioAGDTL+WoJBnXDXOD41R+L5ZTpnf7HJsHtekjxxCX3pvY9D446uuor6pk1Omz8XrceD1u7LU11JQWU557gJLsrKBLw+HSlAk+FHqTBq35HECL17UFiVKik1sXaDUx9KQkEvpGMHBy+2N+T1gi9dRXOclaX4rX4yd/VzUfPbIOn9ePSq9Hl56Gt6gY66xZWE4/vcO5oi69lAGbN2E55ZSgz63scJD82mvE/+0BKors5O2o5LtXtpG/qwprtBFJJTHl/D543X7WfhPSE0uSRMK//kXY2LHB57b/uxpNdDRhY8YgGY1Uf/jREXtfepr6JUup/fprfLU1hxUE15aJINiWaGbcWRntD/T7YfN7sHEB1BW3P85VL3x9AezNbipGtO/5zJBzoWo/bHwLvA4Yeh5EpYugOWEkRPWG6Q8p+l+F3yS/jTUlBYXfIX6/jAs9Onc99jWrSYzQUbJ9O2ETJ6Cx2bDOnNHh/rqMDOqXLMG1fz8GVzURNVnURPTF3FCIqqGaCeatuBpcaBMS0A8c2O3ztNeGvGBPvPQqAGJS0gCYcuHlZIwex6Yfvg6Oyd22hcVvvgKANSaO615685DHKD2QjUarw5aUDIDX42HX8qWU5WRzYMtGaktLSB815pDzaLQq1NoMVNYrUUufYq+tISalfQspg0nLnLtbW3z9XrHGGHHUe6gtd7TYnrW+lAETewWD0PiHH26huc7bWUmv3hFo9W1n4HXpoQDNOFz4KdcHspgqlURlYSNpQ4W2PTLexJCTEtm+tIChUxOJTrK0nrAZmuhoTJMn4djWupvd8UrFq6/i3LkTSaMhbGT3P18VBfVIEpx73+i233tZFsFn2Q5wBv5Ot30kitwOHvfJlbDzK/H8iq9BHygsPOPfEN9achVkwJnw7Z/gu7vF84SRodcscXD7pu5dnILCcYCSCVZQOEa4Gj0gqdD6GvFVVtK4YgXe8nKMbWiA2yLy4otQhYWRf+NNAPQt+A5kPwn5S5EMBkyLFhC54n3Mp0w/rE5tVYX5AJz710cIjxVa4wFTpnLuXx5m1OmziYiNbzE+a12oNXFdeSkl2Vkdzl+SncVHD97LN889Hty2c9kSFr3+Ajt+WUxtsLjt0FrmpuuU1FbOufdBNHo9Mald10D/XhkwsRdavZo9a0KqNUuUgcVv7+LdB1cT8ae/kPHFFy26CJbn1fPNC1tZ8Wn7v0fD4EFirpkzg37CdQHt8Tl/GonRom3RtXDsrHT0YVpW/C+rU/IZXUoqnvx8ZL+/axd8DJBlGXduLvh8yC4X6ojuZ4IL99YQYVOjrd3b8oXqHHhzJrwzWzzPWSl+2vrAtk/amGhjKAAG+PBiyF8nHqdO6vgkwqJCQfKJ90LiH+emUeH3jxIEKygcIxqqXQCEBzxb82+6GYCwCZ0zV9HGx5P47DN4CkSRUcrMcZy07A4SStaQ8MTjyG43eL2YT5za5XPzOJ28eNX57F2zgpoyETBFxIckAyqVmrQRo5FUKiSVilFnnA1AXEYfCnZuD47TGY2s+bz9ZezGmmq+euZRfF4vlQV5fPPcE/w07yV+nvcy4bFx3LbgfwyYLM6/34TJXbqGXn37c+Nr7zLslI6X9f9ImML1jDkjrcW2CeeILG5duYPSnDokrRaf109NqShE279FWJqV5bQvbdElJ9Nv3VoS//McIPyv131zAKNFS3SShSsem8TYM0M3IwaTlnFnpVO4tyY4f0foUpKFdVtpaZeu91jgq6nBX1eHaZIILtuyOOwMG77PoSirhtiGn+GVCaIpxdaPhDThtRMhfw3krgKfB3KWQ0QqDJgFlfuEPKI5W5v9DU64RXj8/hgoZIvoRPOqUVeKnwNmdetaFBSOV5QgWEHhGNFU9BKeHCkq830+rLPP6lQL1SbMJ5xA3AN/BUki4rxzMY0eSdIrL7fw/DWO6LjAqS1qy0pwOxws+u+L1JWXgyRhsbX/ZX7SFddy03/fo8/Yifh9oqL9kkefYcyZc8nesJaqooJW+3g9Hr5+5jGcDfXM/cs/SBo4hJLsLLb9/COy7GfU6bORVCpm3ngH17+6oNPNES7/10SufkrYluvDwg4rC/57ZPi0ZCLihO1Y0oBI+oyORR8mlHH1geztxh9yeP+hNbzxp2VkLhPNQKtL7Lgc3nbnVVutwff6wFahUQ+PEVlhjVbd6vcw+IQEohJMrPpsH16Pj47QJguZjDs3r0vXeixwZu4AIPLyy0hZMJ+I89rrMdU+hXurWffNftKG2phsXSA2vjsHvrgRsn4SbYnH3wR+L3x7J+SuFHZl1iTwucBeEZrM64bMz0LP+0yHibeIx9Yk4ed7KEZfBXftaCmFUFD4HaAEwQoKxwC308uP/80EwBKuJeGJxwmbOIHYu0JaPr/fx+pPP2Tzwm9b7b/m849ZcPfNuJ0Ooi69lH7r12MYOJC0997DMm0aKpMJXe/eqMPDWyxtdxZ7nXA5dDU2UldeGrClat3pqwlJkggLjyCxf0h7HGYNJ22EWDqtKWlZrCPLMovffJWivbs47aa7SBs+igv/8QTXvjCPEy65isueeD6YXdbodFiiOrbrao412kiY9ThoqWuvAmfPFAb2JGqtivPuH8P1L0zl7DtHolKruOaZE9Do1dSUiexvdYn46bJ7cTZ4GDi5Fz6Pn60/dy4ILc+tB+Ccu9pfOlepVUw5vy91FU7Wf3ugQ1mEYdAgkCTsGze0O+Z4oezpp9H06kXYqFGYJkxAbelEkNkMZ4OHn+fvxBpj5NQr+2JUBT5D9cWALLq7IcGggBRi83vgqIa0KRAe0L/XNrvp3PczOKpg8h0QngyJo+GEe4TLwxlPd+6kJEn4BCso/M5QgmAFhaNIUVYNLruHrPWhZV1DhAnjsGGkzp8f9FbN3riO5y4+m1WfvM+St16jcE9L4/m1n39MZUEeO5ctBWgz0E3/7FP6LFnc5nk0+ftmrV3V5uuNNaFWqbuW/4I1unP2avF9+qFSiwIeozUcfZgovnE1NrQYt2Xht2QuXcT4ORfSf+KU4HZJpWLc2ee1aMLxm+X98+H7e471WbSJ3qhBqwsVWkmSRHiMMehGcHBAOn52Br1HxbLh+5wW1mbtUZZXT2yqBbW246+Y5IFRpA2LZtPCPHK2t2+BpomMxDB4MI0r2/68Hg6urCzyrrsef2PjYc/lq6nBtXcvkRdd1C1XCFmWWfLuLux1bmZcMxidt1lG99R/ip/7fgZDOMQPbblz+glgDQTBdYWh7Xt/EOOnPQh3ZYIxQhTF/d8PMOCMLp+jgsLvCSUIVlA4SridXr54ZhPv/HUVe9eJINhaux+1pXX712+feyL42GyLZuFrz+NxCw2x3+dDRgQpzYPVg1EZDEGP1YPZ9P1X7Fy2hGUfzGf5h2+zeeG3FOzeweePP0RlQX5w3ikXXYGkUgVdGw6FVm8gNi0DjVaHVm/AEDi+s1kQXJ6Xw9K355ExehyTL7i0U/P+5vD7oGQ7lO8+1mfSaaw2Q7ClckO1i6QBkZgi9BgtWkzhegafmIAsQ2VRA9uW5rP0/bavTZZlKvLrWxTCdcSMawYDUFlQ3+brjbUu3E4vxmFDce3b140r65iCO++icflyHFu3dnsOWZZpWLmS7LPOAkIOGV0l89dCDmytYOKc3sSmWOB/V4gXLvkEJt0G4QH9rrNGyBj+Xg1J40SAG54UytbWNguCqw6Ixhbq9ldyFBT+qCgWaQoKR4mmQiO300dRVg0Dh5uIf/4ZVFf+p8U4l92O1yOybeGxcZxy3a189uiDrP7kA2RZpu+4ifg8nsDYlhnWzlIUyCzXlBSz7stP0BqMRKekUrx3NwW7d2K0WFGpNYw753wGnTgNrcFwiBlDDJxyEnk7tiFJEvpAEOwKZNnqKsr46O/3oVZrOO3mu5BUv9P78NoCoc2sLYDsJZA6BTQ64dMqqUF3/LVkNkcZKNwjbn4aqpwkD4ri9BtD2UZbgrhZKz1Qx9qvhMfvSZf0b6X1ratw4LJ7Ox0Ea/VqjBZtsFC0ObIs89lTG0kbGs3ghAT8dXX4GhpQm83YN21C9noxjRvXresFsG/ciDtbNJzxlIRWZ/JvvAnjiBFE33jDIedwFxRS+frr1HzyCerwcMLGj8c4fHiXz6WioIGVn+4jdYiN4dOTYcv7ULRZvBjdV0gSbl0HjzZzY1Gp4NqfQs+NUaDSQkOzAsKaPEga2+XzUVD4I6AEwQoKRxCfx0/h3mqSB0UFdZazbhlGYv9IPNu3kAuoDsoEl+wTdkjT/+8m+k2cgtFixWixsv5rUdySl7kVjV6PWqPB1dD1INjn9VCSncXQaTNIHDAYZ0MDv7wzj+K9uxlz1lwKd+2geN8ezFG2QKvdzutxAUadcXZQz6vWaNHo9cFM8Kbvv8LtsDNh7oUYzV3TSv6mqAzYidkrRUHTpNthxj/h8SSxZP2nncf2/NrAEmnA7fRRlltHY50ba7QRnSH0FWG0aDFatMEAGMBe68YUEepoWFnUwPpvDgB0OggGMEXo2wyCq0vs1Fc6aah2ok0V7iSewiLU/ftR9syzeIqK6LNkcbeLH2u/Cvlbu/OF3tnX0EjDL7/Q8MsvHQbBsixT/cEHlD35FLLbjXHECFLeeRuVrut6dI/Lx6I3MtGHaZh2xUCkjfOFN2/qFJj7eijDqzXChe+3r89VqcAUAw2B9sY+r5BGRHS9OE9B4Y+AEgQrKBwhHA1uvnpuC5WFDcy+fQQ1pXYklUTywCjUGhXOOlHwcnDhTNHeXSBJDDzhpKAjQkxqOnmZYrm27EA2Q6fPpCQ7C6e9czpGt9PBloXfEZuWgdthx+Ny0nvMBHqPHkdDVSW/vDMPtVbLhLkXotbq2LlsCeExcT3yPhhMZlz2RnxeLzuX/0Lf8ZOYfOHlPTL3cUtldsvnZc003c31mscR5igRzP48fycarYpBU1p20ZMkiVOuGkRVcSNVRY3sWlVMbbkDU4ReeOM6fXz25EZkv8zwU5KJSe58EGyONASdKZrTlJl2O71oEwJBcFEhhv798NXU4C0uxrVrlyic6waOzO2YJk3CnZuLJzcXANeekMyj4I47MQwcQMQFF6CJigpu99XVUXT/X2hYsgRVeDiy240uPb1bATDAik+yqC61M/uOEYRtewV+fgj6zoQL3haBb3MGntnxZOZYkQnOWSlcIvzeztmgKSj8AVGCYAWFI8T6b3OoLBQZ0N1rinE5vETEhaHWCAmAP5DFVR2UES3cs5Po5NQWlmCx6b2DQTDAiBmz+OWdN4Iyg0Oxe+WvLP9gQfB5WHgEqcOE3ZE5ysZ1L70FEsFjDps+s4tX2z76MBP7NqylurgIR10tg6ee0mNzH7dUHNxYQhZZueOYyHgh0agusTPmjDRM4fpWY1IG20gZbKOmzM6uVcVs/CEHryeZzYvyqCxswOPyccZNQ0kfHtOlY5sj9RRn17TaXtAUBDt8aBOFLt1TWASAL+BgUr94SbeCYL/LhWtvFub/+z80MTHULVqEp7QMx2YhQbCceir2NWuoX7gQ1Gqir7suuG/VggU0LF1K3F/uxzhyJDkXXIj5hCntHUpQWwBvnArnz4eUgBd4+V4aDuxh5woTw6cnkVzwIqx4VrQqnvN693S85jgo2gQLAkVvGiP0ntb1eRQU/gAoQbCCwhHC2SB0vRq9muxN5ciyTEpELa6sLPR9++KrF4VAzQvj/H4fxVl7GDD5xBZzjZ51Dtt+/hG3w05Cv4HEpmWgDzNRU1LUqXMpzxVL1EOnzSA6JY0+Yyei0Ya+YK0xnXN/6A4Gs5nKgjwKdwv/1LThf4COU5X7wBQLjYFlaVmGxmZNIXye465QyZZoxhIliuNGntpx5tBqExrxvJ1V5O2sajVPVzFH6nE1einOrqVXb+GqIPvlUCbY4UVtsyHpdHiKxGfeXytWUuqXLCHmtlu7fEzX7t3g9WIYOgTDuXOpW7iQonvvxVNagnHUKJJefAHZ72ffSSfj2tWyCNBTWISmVzxRV4omEn1XrkDTgY82AFs+hPoi0amtKQj+6mZy90YANzPQ+x6seB5GXw2zngFV2y2qD4k5NvRZ6z8Lxl8Pkandm0tB4XfO77QqRUHh2NNQ4yKhbwRz7xmFz+vH75PRrf6B/efMAcBf30CJ1YRXq8Hn9bBvw1oq8/OCgW5zzJFR3PrWR1z59EvMuuM+APQmU6flEOW5OST0G8iMG25n1OmzsUZ3LVN3OJgiQ8FBv4knoNb8Ae69K7OFb2sTsi/g8xpg0ztH/5wOgSRJnHf/GC7/10R0xo5/Ryp16Ktj6Ekt9amWqM4XUTZhDuiKP396I2W5IritKGjAZfeiD9PgdnqRVCq0vXrhKSrC73Qiu92obTZcu3bhKey6xMSxXfh0G4cMQZeaSvxDD2Fftw5Pbh6Rl14CCMs+w7ChOHfubGEb562oQNNMK3/IAFiWYeuH4nFNXmhb4SbKPH0wSHVE7X5eePme+Vz3A2AQXsBIcNV3cPEHkHFS9+dSUPidowTBCgpHiLoKJ1abgZhkCzqDGr+vFodvF04JCu+5l9Ili9mUHs+nTz3CT/Ne5qun/8mGb78AIKH/wFbzSSoV0SlpwQDWYDJ3Sg5RuHsnxVl7iOvdp2cvsJNMu+p6plx0Bec/+Bin3XTHMTmHo4rHAbX5EDMgtM1R3bJi/7s/HZ1zqSuCxY+IrmGdIMyqwxptPPRAoM9osXowaW5vzv/LGEB0oJNUXS9SM0eGAuc1X+2nKKuG5f8TBaKpQ224HaKjnDYxAU9REb5aIYUID1iS1f/6a5eP6czMRB0djSZeuC1EzDmHyMsuQ9+3D9ZTTw2OM02ciDsnh7rvvxcb/H68OTvQhHfufQKgYD1UBXTiTUFw1X6QfTj84ZjUVUjjroNTHxEuEIfDhJvghl9b3oQpKCi0yR8gJaOgcPRxNnporHVhDbSNveCBcfz0zD/Iirfi14Jp+XJqvC7ok0hpdhal2UJDunPZEsLCI4iI63XIY1hjYvE4HVQVFZKzZQMuu52J513caty6rz7BaLUy8bxLevYiO4kpIpLxcy44Jsc+JlTtB2SIbnbTUZ0DOStajnM1iKYFRwpZhiWPwpb3IHYQDD2vR6efftVAJp3bB41OTUyKhTNvG05iv4huzWWKDOmP83dWkR+QWJij9ETGm/B5S/F5/GgSEnD+8mswCDYOH4YmoRflzz5H3bffkTLvvzQsX4Ezczux99yDt7qa2i++JPKSi1EdZPPnyNyOcfDgFs4S8X97ANnvb2HdF3nRRVS//wFV8xcgabQYDBV4Kysxhm/u/AVu/VBoc5PGQNlOcVOy4S2QVDj8VoyqWtHsoicwWKFX1y3aFBT+iCiZYAWFHsbt9LL26/0gQ9pQsWQaHmOkrlIs2fqnnkCvxx/HrhOa0PFzLmTyBZcF959y8RWdsnzqN34ySBI7fv2ZpW/PY9Un77caU19VwYHNGxly0im/b0uy44mmojhbHzh/AYy8XAQ9q1+C6H5w4XuBcXt79rhed8hX1uuCf/cVATBA5mc9eyxAo1UHpQ+SJJE62IZG271l/CY5RHSyuUXLa6NZF7RpczmEQ4SvogJvudC8qqxWTOPG429owLFpEw0rV1J4551UvvEmfoeDwtvvoOypp9h3yqnUfBZ6D/yNjbiz92MYelDXNWjlXS2p1YSffTbOzEwK77iD/Tc9gs+pRk1Vq33bRJZhzw/Qbyb0O02sEvwrBla/xC7bnynxDBRBcHjnGtIoKCj0HEomWEGhh3A5vGxfWsCWxXm4Gr30nxBPTIoFv8tF+ZtvUlNbDZJEaX4uC1cvITdFLCePnnU2RouVlf8TAcuQTronmKNs9Bkznm0//RDc5mxswGAKZRd3/roEWfYz+KQ/gCPD8YDbDvt+AiSI6i0ycoPnwNDz4YMLoPf0kEyifDck9mCR4PJ/w69Pwg3LQK1rWYiXtQgaK8DUNc/no4VGp2bO3aOISjDhcfko2F3Nknd2IcsyeqMIrN1OL7pE0RbYmSmKLNUREdiuvQZ1ZCRV8+dT+9nnwTn3zzozWETnq6ig+IG/YZk+HXVEBM49e0CWMQzunKuE7eqrCBs9CnnJE+S9JY6tNflE8xP9IW4ua/OFHjxtCoy9FhxVsPwZAJZkikYfRlWdYmOmoHAMUIJghd8Vsiyz4fsceo+KxWjWkr+7ij6jYlsU8vQEfr9MTakdnUGDvc5FzrYKti0twGX3kjbUxpgz0olLt+IpKiL3qqupKC9B7peMVZaoq60ez0QKAAAgAElEQVQJujUAGAIZ2gv/8QSSpOpSF7Wxs89l3/o1weeVBfmU5x5g98pfOOe+v5O59CeSBg0hMj6hg1kUegR7Fbx2AtQViKr85lKHjKlw1w7QmUGlEUFqT7dUbtKavjlDaEsBRlwGo6+EN0+F7Z/ChBt79pg9SEJfIaUwmLRkjIxh1Wf7mHBO7+Dr9lo3EQGv4IblywDQp6WhMpmI+/N9aKJtlD397+B4T1ER0bffhmncOBpXrabilVcof/ElIi+7lNxLRLtuw4Bmuu0OkHQ6wgZlwA/LiB2hwV6uIzzVDj8/DLP+3f6OXhe8GbAbTB4n9L4nPwA7v8KZNANKAucq6yHsEMV1CgoKPY4SBCscVfJ3VVGyv5axs9KPyPyVhQ2s++YAzkYP+zeX01Dtwuv2M2hyzwaBKz/JYtvSApCAQNF4+vBoxs5Kb9Epa+fb89mqdhM39yzI3MJpd9yHQy2RPmosL1wuujg1SR+SBg7p8nkk9BtI4oDBQfuxj/5+b/C19V99Sk1pMePnXtjNq1Q4JCtfgJ8ehL9Xi2xrXYHweD3lH63HNs/C2vpCWQ8HwU1BlNcJPwgHEU57DAzhIiP945+FS8XEW3r2uEcAvVHDNc8IjWxdhQOA6pJGYjLE37Fjw0a0ycmoTCEv7airr6ZxzVoaly8HIPbee7Fd838AGEePxlNaQvX771P9fkg21FQU1yG7vhFykuTx4HNhG+DCNqARxt8Ia18XWusmy7OD2fqRsEWbdDvEDxPbVGq4ZT35m8qBQEZ7wKmHXxCnoKDQZRRNsMJRw+308vXzW1j3zQE8bl+PzJmzvQJnoyf4PDezEoAdy4uCbVh3ry5uc9+u0lDtomBPNbXldhEAA8iieOfCv43jjJuGtQiA96z4hZ+3rKbcaiIzcwtag5GkiZPpN2EKWp2eOX9+iBk33n7Y5zVh7oVIKhXJg4YiqVSMPfs8tHoD6776FICMkWMO+xgK7fDTg+Ln22cKFwZTLMx949BL2zH9ez4T7BCeusx6RuhLI1JEAAww/SHxc+FfhUfxbwhLlAGNTkV1sR1NXByohTxC37dvi3GSSkXCk08QfvZsev/8czAABnGjmfDooyS98nJwW/TNN3Wu3fInV8GOL+DH+yFhFEz7m5C4THtQvM9f3wZ1xbBvMWyYD69NgaItYt/STNCHi5bZzY+lUrFvQxlhVh1TL+7HhMt7qChOQUGhSyiZYIWjxq5VoWC0psTeImDsDjWldr57eRtpw6KZdfMwPG4f2ZuEDtLn8ZM0IJLYNCtbFuUJPaGh9cd944852BLMpA3rWCuZm1nJd69sQ/aLtK9Gp+KkS/qzf0sFUy/p36KYp4ld772Dyu/njJhU9g3IwGixtpA6ZIwaeziXHyRt+Chum/8/NDodPp8PjVaLx+lky8JvSR40lLDw7lXsK3QCbRh47JC7UnTlGnEpdEbOEjsQdnwO/wgHlRbu2QthUYfe72BKdwq9ae9pIgiOGyJ0p0MvEHrVJvpMh+l/F4F61QGI6df1Yx0OTUV7KeO7vKukkoiIC6O6pBFJo0Fjs+EtK0Mb0Ac3RxMVRcKTT7Y7l2XaNBKeehJ1ZFTHHd5qC2HVi3DC3SLQrQ7Il0ZfCaOvCo076zl471x49iBZxQ/3wTWLhEVdeOg87XVujBYtbqeP3MxKBp+YwJCpLX2WFRQUjh5KEKxwVPD7ZbYtyUdv0uBq9LJ1ST7jzkrHauu81+a2pQXsWVPMrFuGs/HHHMpzxZd84V6RAfv+lW2U59ULc32Hl8nn9cFe62bTj7l89tRGxs/OIG2oLagPdju9rPlyPwA3v3Jyu/6msl9m9Rf7sNoM1JaLpdlRM1PpP6EX/Se0bWXmd7moLCog0ucj7d776J9+ZOQfTWgD9k+aQAB28lXX0WfsBHr1OcrBzh8NrVEEwQCXf9H5/WL6hx77PSJjmH5i++PbInspvHuOeNx3puhSZw3IfgxW8a85GSeLILh899EPgte+Cj/9HS7/Enqf3OXdI+NNwbbKKrMZysrQxHav4Uv47NmHHrT4Ydj2Mez/RbQ7BkgaK6QuzelzCvSdIaQwKZNE0Jy3WhQp1uRBXWHwd1JR0MDH/1rHyZcNQFJJ+Lx++o6N69Y1KCgo9AyKHELhqJCztYK6CidTL+qPzqhhz5oSvn1xa4suTB1RXdLI8o/3UpZbz09v7WDbkgLqq50AeJw+ivbVULBbBMNz7xnNhX8bR3SShYR+EfQdE4uzwcMPr21n/fc5gAiA5925LDj/1y9safdc9m0so7KwkXGz05l8Xh9SBtsYNVO0IfX7faz76lNK9+9rsY9z107qdRrix01Af4QD4LZQqdSkDh2Bzhh21I/9u2XDW/DcEPB5xXO/D5zCr7ZLATBAzEHNUBw1XT+fpgDY1kcEa1XZoDO1Pz46EPhW7mt/zJHCHrAT+/jykI1bF4jqFUZDlQu30xvcpok5/K6Hfr9MY62r5caCjbDtfxA39P/ZO+vwKM61D9+zlmSzSTbuSiBACMGCW3EqWAvUS4G6n5aetl+9pz2109NDlVKhxUpbpEBxd4cQEiTu7p6V+f54I6RYcJv7unLtZvadd96dyD7zzPP8fpB/VFykDHoDpq47vRJEQK/6RYZA6yHQ5QHx/aH5IhNcHwSf2C264HYsSmDPsiQc3WzxDHI8dT4FBYUrhpIJVrikxGzKwMZeQ5uopoaTuhqR+XVwsaVVF3dcfOxZ8c1hinOqyDxRgl+Y81nnTI0tZPmX0djoNVjM1sZg9543e1CQXsHi/xxg4+xjqFQSD/67N/ZOTcL7Gq2aYVM7YLVYWfjJAfb9lYJ/W2fSYps0PoMj3UiOLuDEnlzCejSt21xnYen/DpGdWIqLjz2tu3oiqSQ6DWmq98w6cYyt82axVZII7z+IPnc/gIOLG/m7d2NRq/AMP1WHVOE6ZfkL4jFth8jalmeD1Qy3fy7KEc4Hl2CQVODTGTL3N3eTA6Etmx0tyhvUp/k3XVPW9HzKWsg8AHPvFEYMZ8LGABpbIdF1pTHXB5pqLXw/BAa+Cn1faLE9sLOXCO5Lcqsat2k9PC56WdHr09mxMAGvECfueDZSlEydWCXqdyevFAH7rm8h7NYzT9LQFOfXtX6xQSIw3vSB+N7BB6tV5sRe8TOurTJTV22m/z1hLatJVlBQuGwoQbDCJaO6vI4tvwoDAFcfA66+QiKqIePadWQgKrUKV18D97zVg1/+bycHVqWcMwjePPc4yNBvQmuSogtIOpiPrb0Wna0G90CRmSnJraJVZ/dmAfDJqNQq3HztyUspY/F/DqLSSIT18GLwQyIj98dH+1g3Kw6NTkWrzuLDtSCjguxEkelruIX5dwpSUwDoOHg4sZvWcXz7FsK0enTH4yHAA/cOShB8w2DjCLVlsPwf8PjWJkmyC9F3VWvhrWKRVX7PrbmmL4h64T8mi9reO2c2ba8uATtjUzZ34hxRSxw6WJhytLvj7Me1NV5Y1vliqcgVmdKp64Vl9Ib3IH4tjJshgsZz4BHkiKSSOLE7l4YCJLXz2f9vtISkg3kA5CSVUpBRgU+oUfxcHX1F1je4/7nLVAJ7w5O7mttkT5wLcUvERUnEeDJPFFNZUsuwqeE4uNri5GaHncOpfQQKCgpXFqUcQuGSkRbXlGFqyNZaLNbGbcGRTbcvNVo1nQb7k360mIKMisbt5joLZlOTckR1eR3lRTV0HSlqcN38mtvManXqRqvW8H6nNsqcjN7YFCBbzTI9x7RCUklIKon+94SBDKtmHCE/TdQaF6SLxwc/6I1XiNNp58w6FoeNnZ7O2HBLQRVuBcXEVpdyqN4Iw80/8KxrUrhOKMsWAbBPZyiMF+UHDUFwC4K4M6LWCGmzkzPBFjNsrM8iJm9u2p4dDR8FQtyfTcoSrvUKCZIEo78UrmRnw84INVcgCF72PCy4Hz7vCFs+hYo8MHiKgP2un2DsdyJA/KYvHJonMt9noqoIB4OVkE7uJOzPxXXqVABUXj6YL0JlpqbSRG5yGaHdxN9qWX29PyWpbCya0qyR95x4tGuu/mDvClFThFKHWygnduWgs1UT3NENr2AnJQBWULhGUIJghUtG6pFC7By0OLrZNjarVZXWAdC2l9cp9W8NTSErZ8RQUV/f+8v/7eC39/c2jmnIxAaGuzbbJ7BDk7D8yMcjGPJwe/zanT0z1GVYIAPvC6PnmBDGTeuKwbkpKPYMcmTi61GoNSoOrkkFIC+1HBt7TbNxJ5OdcJyjOzZjzM4j/z+fYfTw4o7nXmbiG+/jFRqGd2hYM/c2heuY7HrJq6HviYxw3FIoFr8nOF1kd7/BE8pzmr6Pni8yvW5thOJDQ4CYuV88Hl0GsUvAwVvUA58PVyoTvP8nsc6SVJH1Td0G9vUXwZIEkRPhie3g3RGWPCFkxk4XCMsyfBwMP43Ezd9AZWkd9reNot2xo2z4PY3lXxwSxzlZCaOFpB8tQpahQz9fJEn0HQCU55cRlx/Jhl+OXswZaEZmfAkB4a5odBdmK62goHB5aFE5hCRJnsAfsiyfVsxQkiQNkFT/BfCMLMsxl2aJCtcDVqtMWmwhwR3dMJus5KWKmsUGrd7Qrqd2QTcEl2X51Sz69AATXouiutxEdXmTjml2YikqjdRY9mD00DPl036odU3XbzZ6bbNa3jOhtVGfNVvs5udA+z7exG3PprbKRHJ0Af7tXE6p26uuKCdh707WfDsdldVKx/wyQpYvwyZUBCSOwL3v/+ec61G4jsg6KGp4fbtC+BjR9KTSgKMfaE5/kdRifDrB8RWi0U6lFtJcPp0hfJzQIa4tF0oPZfWZyaJkyDoAff9x+nrhs2FnFPJfl5OG+l+NLYz4t1j/2rdOzZgbA+ChZbDuLfGeWw+D9qNgz0zhrNfpHhFEA2QfwqmjCJJL86tx8zNQmllAQa5MwZz/w+3WKdDnufNaZlpsITZ6Dd6tjcgyHFidhpOrjoq8phKm6PXpWMxWwnp6nbHU6myU5FaReqSQiuJawnooTaoKCtca5/wPKkmSM/AzcJa2YzoC82VZ/uelWpjC9UVuchm1VWYCOrhSkltFwv48SnKrRKYGsDee+gFycnBZXljDvnrlBoCNc48ReYs/2QkleAY6otE2ZVBsDdrL9j4Cwl2J2ZzJup/iqKk00bZeAk2WZYqyMqgoKuTPT9/HVCNunfoWVxD0wb8bA2CFG5Ssg0LRQacX7l95R4VxQuTdFz936GA4NFc0t3lHinKLfi81OczFLYGgflAg6u3J3CceO99//seyNUJu7MWv+WyU1wfrt/2naY1tb2/KBJ+MSg1D3oETa2DLx6LJbPVrIgve6R5I3dE41FgXB9hRml+FW8ka6gpqAU/i6m6n/9o3wTlY1O9KUpNJyBmQrTJpsUX4t3dBpZLoOSaEXUuS2DgvAZiIs7MZE/Zs+z0eEFnj0c93Pu9TsXHOMbLiRebdwdX2vPdXUFC4vLQkjWABJgJ/nmVMT+B2SZJuAWKAx2RZNp9lvMINRmpMAZJKwr+dC+p6Hd65b+1qfN1wmiAYYNCD7YjdmkluchnR69Oxc9BSXW4ibmsWyYfyqa0y02mI/wWtSZbl8+6+bjDwSIkppE13TwLChYHB8R1b+Gv6JwC4uHvSPikbx+pajKNHY7hl4AWtT+E6QZZFENy6vt7WrbWQy7pUhNwisswJa8HOGWSraCJrMM9Y+owoweCk32UHH6Ewcb7YOV/+coiGTLPjSVblrq3OPF6lFtn1zR/D7m/BUicCfotJBMFae7CacSrbCQyiNK8ajvwftVZhinGisje99DPR/vZA05y9nobh7595ifnVVJXV4d9OnOOuI4KIGOjHr29tobwURt+rR9+hNxXFtRzekM6hdekUZlY0Nvu2FJ1d00esk1vLNdEVFBSuDOesCZZluUyW5dJzDNsLDJFluTugBU7Rk5Ek6VFJkvZJkrQvPz//lAkUrl9kWSbxYD4+rZ2wtdcS0MGFwAhXvEIcue2pjvS/u80Zs7ftensz6tlOSBK4+Nhz79s96TjIj3a9vampNGO1yHi3On/HM1mWyXj6GdImT8aUk3PuHeqxN9pgcLbB2UvPgHubJIwOLV0EQKDekU47D+FqY0erxYvwfuN1VDYXeTtc4dqmNF2oN/h0ujzz613At5tQS2hwJnMJBv1JLoaSBLWlMH4W+HWHMV9f2LHsjFBXDqbqi172GWkoYXA8e6NqMxx9ARm2/ke48FnqoChJBMEhA8C/O7rMTdg5aCnNq0Quz6NWtsc71Ilaq56kmnqt3t7PQmAfEUybqinMqmDZ9EOnaAE3mN40SK8B6Gw13DMmjQfcHsXePxBJknBwsaXL8EAklUTC/rzzPhUqddOFi9FLKYdQULjWuFQSaYdlWW74L7MPaP33AbIsfwd8B9CtW7eWOSQoXBfkJpdRkltF5CDRIKTRqrn9qcgW76+z03DbU5G4+RmwtdfSb4IQ9Xf2tmf/yhS8Wp391ubfMRcUUJuQSMX69QAkjx1HyLKlaNzcKP51AXXJyWh9fdH6+YFsJffjj1HZ2uE06g6c77mHO57t1CjBZi4upjQ7i8yURFrnFNE6NxH7Af3xfvc9tJ4Xr1OqcB1waL54DDl/p7MW03qoUIQ4sUp87xICdZVNr989X2SLA3tB+NgLP45fvVV3wrpzy6ldKIfmiaDW5SzZ379zkrUwg14XJRHHVwgDkK6ThCvfpg9xctVRmlNOnVUHSARHulOSXU6q8UHC7vknBPXh6MJV5BZvpPfuuaxcE05pXjV/fLSPkY9F4BEomnPLCkQQ7OhmC5s+Ek2H1UVoi5LQ6kqbZbHtHHS4+Ah5xeryOmQZ7BzERf257jTVVJjwDnVixKMRp7VWV1BQuLpcqiB4tiRJ7wNHgDHAB5doXoXrgOj16dgatLRpQXPamThZ7aGBzkMDiBzk12hz3BIKf/yJvI8/BoSOqMvkh8n/33RS7rsPtZORmsOHT7ufLjCQvE8+pWLTZgJ+noWkUmHKziZxxEhOONuDpzPd3/4Xjg6O2HXqhKRRJLZvGk6sEtlFt8tY9x06GDa+D3u/F8YMBo8mZzoQElwN5REXQ/AAUZsb88flCYKzD0PKVlHnez5Ne44nKWz0eAK2/bdJJi6wT701tYxRX05GhoZaG5HBtdFr8A9358QeK3a73enjb2XDWh0wnLxfEyizimC3oqiWhR/t57EvB6JSSZQVVKPWqtBTIEwtnALAJUhc6AT0PMXEwyPQgeO7c5j1ynaQISDchdyUMgbe15aQTqfWOicdzKeuxkxNpQmjh14JgBUUrlHO+5NckqT2wL2yLL9+0uZ3gXmIorWlsixfwoI5hWudkrwqPIMdhdvSWZDr6sj/5hv0nTphGDCgRXOfLgCWzWbKVq5C37ULGi8vrGVlqI2iZKJ4wa+N41wmTcLtkUfQ+QdQNHs2tUeF5FHArFnYtA6lcts2ShYuwmnMGJxGj6Lkj4XkvPUW5WvW4jhiOKV//olcW0u2TzA+Xt543XKermAKNwbFKdB+9OU9hvdJTVfdJotHtQZGfiwkwC5FANwwZ/hYOPCLcJ2zvUDbXotJGH40UJIOsgV2fSPKGbo+dH7zNWRetXpQqUQjYOwi0NgJGbX6rLiTNo9jZV5UuYjzYaPXEHGLHyf25HJ4YwZOHk0lB/nmUPobf6Sw1ofY6hFYrTJx27Kwc9ASvzcXJ3c7pOh5YvD9C8G9zRmXF9jBlYR9ebSO8iQlpoCUmEIAVn4bQ8RAP3rf2aqxebem0sTGOceoqTKhVqvwClaskRUUrlVaHATLsjyw/jEOeP1vrx1BKEQo3IRUFNc2agBbzGbykhPxbh12yrjCH3+i8JtvKQTa7NuL2nD+GrqW0lIqtm4ja9o0UScpy0h6PaGrV5H7ySeYUtOwjYjAnJOD8733AOA4YjiOI4Yj19VRm5yMbZhYm9Po0TiNbgpujHeOo3jOHHI/+ADDwAGULl6CrmtXKswldOo38PxPjML1T02ZsBl2vsymJyqVkBArThGWuw30eEx8XUoixsOe7+CjIHguGowtaDxNWCdMPXw6Q84R+GGokGjr96L4O/y8gxin1kGXB0UD3vlg6wgjP4FW9SUnHu1FEGwMEMG2nREcvHGSkwEv8kwhgJBH9Ap24r53evLbB3vZuuBE45S3tNtJe281nPiZ7h1smLVmEJvnHW98ffA4I6z9VChXnCUABmjV2aPRSTInqZRVM2LoM741uUllRG9Ip7KklkEPtcPGTsP8d3dTU2lCpZKwmK3YGpQssILCtYpyT1fhojCbLNRUmLA32mC1WNjw07ccXreKCS+/iX/X7o3j6tLSKPjmG0xuLpRXV1G5dSuOI0e2+Di1ycnkvvcelTt2AqAyGHCZNInCGTOQq6qI7yesTV0efhiPF/9x+nIFrZZKezvOJFQkaTSkDeyNzZxfUY8fT11qKnb3TISVC3FwO428k8KNT0OTl/EKOP89vEo0g9lcZoMVv6iTLKCfF1nQs3F8Jcyvl4KbMFuoWJiqYOO/YOunQs6tAUud0Pu9EHo82vTcrb6tRHXS37F7W5wKDgO9yDYJu3Nbe5GNNnrqueuVbpTmVWFrr8UrxAlJVX/nZt5E9MlLuOvlaZhNFhIP5OPgYoN/7GNC4/m289P09gpx4qEP+yBJEq27eWKxWDmyOZOkQ/kYnG2oKq1DUknc9nRHln95GAcXpXFWQeFaRXGMU7goKkuEI1xe0mamP3gnh9eJxp69n4sPluqYI6Q++BBJY8YiaTTsbB/CrlBfMt98i7JVq1p0jLrUVJLHjqM65kjjNn1UFO5PP0XbmMM43iFqG10fewzPf758SgBcXljA0e2b2TpvFrNefJKjWzee4b0Us3/PNna08cOcl4/Hyy8jdRGKAA4up9YsK9wE5NT/zl2IHNn54ugNQX0u/3EkSbi19XxSZHj3/Si2n86xra4SVrwMBi/w6gi/PSBKKYLry5nMNbDrq+b7eF2Cm4INxhrGgKZtQX1wKtkCQHJtDyQJjJ5NsmMu3vYER7rjHWpEUp3UsBZ2K5Sm4SHF4tPamX4T29CpdQak7YRBb4DD+fcynNwQ59dWZL3tjTaN5kDjX+1GQHtX7n+vJ+36+px2DgUFhauPkglWOCdZ8cW4+Ts01vzKssyxnTn4t3OmsqQG2VrB8e2LcKs141tURprBhkJVLUU//0zuvz8EwG7AAIz330fFl0Lbs9bfh+zX/g+7yEi03t5nPX7JosXIdXWELF+GpaSUgi++wPOVJl8W7/fexXjnneh7dG+2X0VRIet//JbywgJyk+Ibtx9YtYx2/cRt19zkRPROTji4uJGblNA4RvXZx7j26U/in38AYHB1Q+Em5MDPwoTBq+VqJ9cFxgAY+IpoYlv+gjDqODgbXohtbgO99TMoTYNJK0Tw/FP93Zshb8PM+tKF6uKm8Xq3CwoqT8E7EkZ8BBF3NW3rOBGbDf/CTlVMtdUZo6e+mYnOGWl7O6x7GxY/Cs8dhvTdMKtexfMSKH4EdXCj221BdBwozltabCFufiKb7+iqaAMrKFzLKJlghbNSXlTD4v8cZP47os6tOKeSPcuT2fDLURZ+vJ/8tAqslgJk2UpoYRm9v/ganx49qbTRkvPvD5GB/W0CWFiSwY9ff9I4r/TAfciyTM7b7yCbm7rgi+bNo2ju3OZrWL8O+5490Pn5YdchHP8Z36ILbLo9rbK1xb5nj1PkiqLXrSRh705yk+LxadOOoY88TWhUL3ISTpB8aD81lRXMeeU5Frz1T0y1NcTvEaUWju6erPr+K7Ljj7N13iwAHFyUIPimI+eIyBZGTRE1uzcatk4wZZ0IOA/OFtsy9jYfE7dElDcE9RH6xA34dml6LtWfm1tehylrRLB8sUgS9Hy8yTUPROAe1A83jShRcfU5m4npSdi7wpC3oCRNKHD8OLzptUuQ4VdrVfS4IwQ7Bx12DjrCenqft0mPgoLC1UHJBCucwsKP99Oujzft+/iQnSjcpSqKa/lp2jasVnHLVJZlKopr2bkkEZVVZILCpn+BXceOeGWlciQ2mrKht7C3LJ+6eothj6BW2Dk4kBJ9gE0L59N7/BjkX+ZzrEMEnq+9hvN995L77nsA6IKCsO/eHXNxMXUJiRhHj4aKfGFc0PABfHCu+GC75dVT3oOprpaYDWsav7c3OtNxyAgMrq4k7N3Jon+/1fhaaV4u0x8aD7JMxyEj6HbHOH6Z9gzzXn8RgI6DR6DRKc0tNwWJG0VQFzJAyJVpbKHTfVd7VZcPrS1M+AW+7A6WWshvaizDVC1qlDvUZ2PVGpERNvxNHztiPBxeAK0Gnd0Z7lIQeQ+qg8KRLqjjeVyYthosHrc0XYjT7o5TpNAUFBRuLpQgWKEZddVmcpJKyUkqJXZrFqV5VagxM3yUkZQMFZ7tffAI0rPgrSfwCu1LTmo71LWZqJBw6ShuGXu0Ep3W2/PSms3dafhtdBg4hMLMdH5/9zUO56TTC6Grl/vBB+iCmrK76VOmoo9ojW1UPyyShC7lC/j2U6gqgGmJonN8+QticNjIU9y8Dq3+i8riIu74x6vs/GM+UaPuBCA4siu3P/8KIJN5LI7shOPkJJwg4pahtO7Rh6COnZFUKvrd+xAbZ32HR1Arhj769GU51wrXILPHiMdnDsDh36DDnZdOnuxaxTkI/hEHM/pDXmzT9vxjwsLZs33TttPVLPf9B3S6t3l2+HLRfjTdXW9DowkltOt5mNUY/eGeX0WpRtpucPC8ONMRBQWFGwIlCL4JOLE3h8riOjoPCzjn2IqSJnvRoswKgkJt0C76kZpNu/ECnO+9F8vd46mpKCfl0EoMxkNUWLJxMjgi1d8y9gptQ5te/SjOyqCypJiqUpFNdg8IAsDV15/eE+5j7XdfUh0SiD5J3N7MeO55tB5G/LueoDzTlvzD8R7awfwAACAASURBVFTFxBPdzoM1CQae7BCHrdUM2/8H2z4TzTkZ+2DOOHhmf6MsU21VFXuW/E5QZBfa9OhDmx5NH9ySSkVYr74AhPXqB0BdTTU62+a1e52H346Ljx8+bdpewBlXuC6oq4JdXwtJL6sZDJ5Nr82bCKZKiJp69dZ3JbF3A68IkfkF8Xf1fX321LPD2fc1eIDHFfo7sTHg8dJiRtg4gPo8s7hh9fXMPp3PPk5BQeGmQQmCbwLW/hAHgLO3nqCIs99CrDwpCO44yJ+25gPssB6Hnt0x2OopnjePvO2bwWiLa3kVhXIWNmYLfcfd3bifJEnc/tzLYr7iIlJjDuEeGIxHUIj4kHUOpnXXKNapVFRMGEeYtx3pz32ASqvGPyoBGycJm0AX9MEyZXGl5OjELdYltaO50+Y3tNs+A50D1olz2fDtJ4Rn/4j33h+gv5Bq2v/XYmoqyul794MtOj9/D4BBBMtBkVcgs6Vw9Tg4Bza8J74Ant7f9FphPPh0uTLZzWsFYyCk7hAqEevfadruEnL68b5dhd2wrfHKrK+BGz0zr6CgcMW4Abs9FADy08tZOSOGjGNFjdv++uowxTmVZ92vIQgePtEH3ZK3iJ/+Ocd8XNlptMX/22/w+fZrSut/a4aPHMuwGhV3dOpN29tGNZtHkiQkScLg4kr4gMEiAN4zE6Z3hneM2H0eQmB4BAfXrmDGjwso7aTBf0ABWj8v5KnroNfT6O3SkdpbAfDycSMzIZFVVUPEAbpNIi8jm+idB/grJwJTyh5A1ALv/2sJrXv0xjPkMtrcKlzfyDLs/6n5trVviMfOD4DOAfo+f+XXdTVxDhLawdXFoK1vOms97MyNbvcvgkc335hNgwoKCjcFyn+vG5QTu3NIOpjPmh9im32GLf8ymuKcSixm62n3KyusASBtwy9sqilhl6/Qxy3OzuTLyRP5/pv/EO9oi8HojPczzxCxdBmBb7557gUd/h1WvNRsU1iHVljqlSH2SL7Y2RextHIkv3w2k2r3rgDMSRG3LnvfPYme4yZyIqmI471mUhT+GPuWLwagtEbNvugsAHLiDlFXXU2HgUPFQTa8D190FQ1PCgoApZmw9GnIi2te7nB8hWgCu/1zeC3j8lslX2s0aPPOug2yoyGwr2iaOxN2xlNq8RUUFBSuJ5RyiBuU4twqAKrLTQD0ndAar2An/vo6mnlv7wZg1HOd8G/XdGuxMKuCQ+vSMDqXcOREDI4mC1ZPV+zUGjoOHk55USH2RiMGZ9fT2iKfle2fCxH9Scth9wzY+D5hpSuJNxSSVOGKyaphX8A/SVy9A4CktBJCdKLxRZIk/Dv3xtEvl10Lf2X5j78A4sO54+AR5B/ZTmphKb2+jCLtaA0QgE9dHMRlieNa6uC3h2DyquZNPgo3J3/9A07UG7UMeQcGvAKp28DJH/y6Xd21XU38e0BwfyENV10Ene4BraJzq6CgcOOiBMHXMLIsX7DeZHF2JaFdPdDoVBzbmYOh4jCewbdx58vdWDb9EKX51WxdcIKxL3XBzqBj34oUdi9NQqvNISdpHgDtXT0ZOH0mFpMJG73+4t5MaYYQvrd1gt7PwMYP0CavYezAwZT1eZOZr77O5tU7cHT3oCw/j91LfqfE2AmoZtxjD6HR6XDx8aN1995YrRaCIrsS0qUbjm4ebJpRw6FN5WxN1hBd5EKAfQm2q54Vx9W7wV0/wuLH4JteIhC/7TPRBKQ9k4GywllJ2QZLnhSuYzYOV3s154csQ/Zh8bzX08Ki2MagKAWA0NN9aBmYa8XP+Gaqh1ZQULgpUYLga5SdSxLJOlHMqOc7o9WdXxf08d05lBXU0NZpD+GPPI50aDae2+ZQ7bcIpy5RDB2STH6RI1vXSuxZmkzb3t7sXpqELMuozesb5wm8awIarRaNVnvug+79HmKXiEza8ZVw3+9NlqemaqgpaXKS0tqJDm2DJ0z4GUeNDbc+Ow0bvZ6gyC78957RFGdnsisbVJIVj/CegMgIj3rxtVMO3eaW2zkRHcueDHDQ1HBLlCf0fQEcfcG7I+js4f6Fwu0q5zD8MARUWgjoKSxVo6aAxqZpQotZ3A7OPyo0UE9+7XyR5UtjHnC1sZjAahEXDhveh5JUSN8DoYObxhxZKJqkQgeL34XAPmBwv3prPh2FCVCeBSM/ge6PXO3VXJtobJr/XBUUFBRuUJQg+BqkpsLEgVVCNmzb7/EMvDcMq0UWQapGRW2VmaqyOgzONo1WxicTsykDW2s8Nck/Y2cZTZfCn0jf44xl2UPkP3onB3YdwEVtJrjbS8Rtz+LIFiE+32WwmZ0Lc9FYrJjVKnx69GrZgrMOwer/AyRhwwrweYTQWs3cD4vqgw0Hn6Z9pq4TpgT1AWK7PgMaX/IIbkVeciIPffoVWq0GvYfvWQ/v06Ytj379E5bqclRrXkMaMK0pAG/AMxyei4bYxUJf2KezaABa/SrkHoExX0P6Xsg+JGSzGqSiADrf37Lz8HdqK+DbvtD5Pug/7cLmuBaoKhIass5BopylwSyhKAks/YW6QuS9sPIVUXrywGL4/SEI6ifGX0vErxWPbYbfGBcnCgoKCgoXjBIEX4MUZJQD4B7gQNzWLCwmKwXp5RRmNld20DvpuP2pSNwDHKipNBG/Nxc3fwdyU8qwqVjLHosvdn8uwCHFmU1tAzCrVdTtOoid1UwRWop2/w8743MgqfEOsOXoov+hrzUxtkMoOs9E7HVyyxa8YhroXeGRDVCcAqnbYf27kLylKSgGcPRuen4Wp6Y7X3uX2qpKnL18zjjmdKjtHGD0F2ceYOcMXR4Saw27FdRaEbjtmSECooNzxDiDF4ydIW75//kUuLSCwBZeEJzMnhlQnAwb/nV9B8EJ64RTX2m60I9V17vn7ZkJ5TlCtzl+HVTmie0/1DclZuy79GtZ/gKoNHDrJ+ceezoy9ggpMOfAc49VUFBQULihUdQhrkEqioVM2bAp4XQeGsDxXTmNAXCbHp70Hd+agfeFoVJJLP8ymqqyOvYsS2bLrydY9Ml+HGwrMVWJ8ZtXb2Obsy9VNlrUWIlKyiIou7TxWE65KwEwrn2bEtlCl1698dLMwaVwJ+z+tvnC6k4jr7b7OxFYdJssyh0CegoHKRsnETyl7Woa6+jXovevd3Q67wC4xajUoutfXV/i0e9FYY17cA50myKy18/HQOTdIoMLMHc8JG06/2MdXdb0vDjlYld+9ShJbXp+aB5U5ovnBcdh66fieYPTmE8XYTwBYK4BU82lW0dpJuyfBTG/izKTCyE39tzmDwoKCgoKNwVKJvga4eQmuIYg2OBsQ+87Q3G3RqNPXYL7xFfR+Qt1g+ryMrJPnCB+vzfrZsVRXi9tFtTBGZeYV9lh407brEK0rW2IwYBOJfH4I1EQMpSk+GKO/vI9AKWqo0REx5MdoMdGr6NraA7E6cDJTwQcvZ8VjUO7voHVr0Hb26DnUyLYXfsm7JgODt6idrYBSQKfSDhWfyv89s9F+YF7mytzMs8HgzuMmykCtoi7mr828hPo8QQsnALz7ha3+d3Dmov1yzIkbRSlFbIsbGZlqyghyDoI3R8T9dL7foSh717Z93axRP8qfu4GD7D3gJCB4mJBZw9tRkLiBrDUwmNbYPZYsHGEiXNg80eg1cPub0T22K31pVnPgV/Eua0uFqUYrq3OvY8si9KM0gyRAS5MvPmkzxQUFBQUTosSBF8GTLUWkg/n493KiIPLqQoEVouVX/+1l4gBvnQY4MuG2cc4viuHdr29GXhvGBXFNdjaa9BUZcKKD2kdO1fsuKkSHliE2WRi7XdfEr9nB65+bUk5GIS59jB97nqGHs7LmLvOFj0mgvJLsMeEbY0jxtv6ohrxLwBC28DD7dpQ8/0ofk0MI72NLfm1VXTsFInmyHQR+LUeCrPHCHMLn84Qvwbc20LyVpHhVNuIAChqKoz8+NTyhts+E01ovt2u/VvP7W4//XatrZBUe3CpaKr7aYTYfsd0YbWbe0RcGCRvOf3+xgAY/AaUZ8P+n4UaQW35mYO3siyYNwHu/PHauGBI2iRqpAH8omDkR5ATIxoGDe7wxA6wmsCjnai31tiKDPuo6ZC6UwTBsUuEHNmEn8WF1bkw1Qi3Nnt30TgpSXBiNfh3hwM/g1sbKDghNH17PyOaLtW6M5fXxC2BuD+FKkjWQdGUGTzg9GMVFBQUFG4qlCD4MrB3eTIH16YBp2rxAhRmVlKcXcnuhXFUldVybEc2AHHbstDoVGTFl2BvyqDuvTHoHCzQfxpyVRHSvh8omH4bKw5DfrmE0aCmMOMYcAyAbQtexa3yOCW6Vni5e+Ec1onSZctxt1YTOPDWZmtwCWkPd39AjxnvsasgEJCIyP4OHGyFsoLBHaashVWvQvxqUcoweZUIcqLnw5HFEDYSej11+gYjt9aXLgN4tTG4w93z4Kso8f2yZ8U5yDsqVC+cg+CeBSIQk+orjCSVCOJ0eujxOBxdCp/Wn483i04ftB2cI4LMnV/AqPra5qIkODBbNBgOefvKylaVpIufe3WxCD71LvDgEph/DwT0BreTHPn+LpXW0Ji4UVx4kbbr1Ez76VjxYlNttksr0cC262uwcxHatbd9Bju/gjVviLKGlcKemylrRM13A4WJTb+7LiHidUXzVkFBQUHhJJQg+DKQGV/S+PzojuxTguDUI4UA1Jo07FuRSojdPoa6zWBx5ecc3pCBVisTengNyYXuhL7Si2yfsSx4659EuQZx4JiMjVbi1qBa7LYlkaF3ZI9rAGCDLFexhQBqtBq8O3fF5/Fn8HzjDepSUrENP41JRMcJRPTZyK4/0wFws6mCqGebZK38u4sSgGXPQc8nhUMUiOzvyU5bNwMnZ2b1rk0B8PhZENCrSf7tdAT2Bo/2wqEMYNO/hUGD+m9/fjn1+rUF8eIrZStsnw4laSBbRAb+igbBaWLtg99oCnIdvODRFrjvOXiLDK1aB3UVUJR87n0KE+HQfAgfJ8ptYv4QATCIANjRTwTF3h3hv+GiTKVK/C1xfJUwd2jgj8nimEPeFiUpSgCsoKCgoPA3lCD4ElNZWkteShmdhwVQVVbH8V05eAY50mGgL0kH88k8UULslky8DWm0VS2lyBxAlOtKNKYC7tA8QZrvMPS5NZTnHqRMZ0PWXkeOVgrt3r2F/rjrdHTNLoO9mdQ5uuBZWMLQzHjUVpnoCC8KjQa8g8NoO0qI/6sNBuw6hJ9xvY53f0mbtLfwbheJFHD3qaYBto4w/qfLdr6uK545IGpS3VoL+bOiJBGQnQtJgjHfwKzboa4ctnwibvGP+06UEoDQ4E2uV9JI2wlfnuRcducPsOlDUQZwJbCYRT13aRoYx58qN9cSVCoY9i8hTbfwkeaSc2diy6fiTsOID8HBE3o8JuT3Dv8G+3+C3k+LDLqTn9AgTt0OrQYJTecDv0DHieJiIX6NKOMY+Qn0ePT8166goKCgcFOgBMGXmOToAgDCenph9NBjqrGw7Y94Dq1La2x46zjQl15JU9HY6cFTB8NXwMHZ2G77Lz4Zy0nf6oKulQ1bDP64xR+hukjo+DpU19IxJR/HqCjsJ0/Gcdgw1OteJGPmdioytdwZGI129OtI/f7R8gWr1Nzxyr8u+Xm4ITm5ltfG0LIAuAGfTvBqOix+XAR6x1fCr/dBmxHQ83GoyBeZ5XHfg0uwaEosyxI2tuFjhb5x2k4RLJ9FXu68yDoEGXuhIg882kLoUJF53T9L1DGDqAe/UHo8Jh5dQqAo8fRjzLViDQnrIXqeaLp08Gx63aeT+BrxQfP9fLuK8zHsX6Lees8MiFsM2/4rSkqgZeUXCgoKCgo3LUoQfCk4vlLUMQ77F8nRpTi52+HibY9UcIIh9/mzpLiGvNRy2vfzodfoVtjWpMCXhTDiHejygJhjyNtUG4eTMflRbPyM2Lz4DMz4hQIHPZrqKnok59Dm3gdwf+F5JNVJyna3/BO/uN6YXbqiLcm6cepwb0QkCcbNEM83/EtkhHd9JQLO0nTQOQinLr2LcN47Ge96tY0tn8DAVy5+Lak7YdZtInP6d1oNFrW3IQNFTfPF4h4mShsa3PMsJtjznTCuSNsF5mpRQx06FPq/1LI5+/0D2o8Rmeah74gg+OQAOGpqcxUPBQUFBQWFv6EEwReDLMOiR7Ee/oP4mn6Y078nI2UIHQd6Ia19A3Z8gdYYwB09nyAn1EBgmwKk7IwmU4H6+k5rVRVlq9eQ/eqraHy8CZj/O4knjgKgdzIyrFU4TkNdcZ06pXkADODZHqn7ZLT7fhDfu4ZyrXMw7yCz42YzNWIqYc5hqC9VZvN6ImykCGgBYhcJSbFRX5w5cOv7Auz9QVgVXwoSN4jSjhdiQe8G79dnX4e+C32euzTHaMCrA+z7QQTdD68Q+tNrXhd10l0niWx3YO+mmvOWYOcMfl3Fc62dqEHOiRFZ56f3NTUoKigoKCgonAElCD4fSjPFB3h1ETgHQ8pWLIlbWaf9loRcdygFSSUTUfE5xP4q9ilJw3bzqwQBxDZNZTI7UvDVAqqjD1ObkAAWCyp7e/y//BKNqytlBSJQfvi/32Jrbzj7uvpPE0GGnTO4hV2Od35RnKyBvDl9M09veBqAtalrcdQ5MnPYTNq7nqZx70bGtytMS4T840I5ovez4HQWe2i1VtS/Jq6/NMevyBUyZH+XLWt7Brm4i8EzQjymbhdZ4J1fC5myh5ZeumM4B4uMes8nL125iIKCgoLCDY0SBLeEoiRxq/XQfJE9s3eHihxkGdbzXxIy3InqXovu6DzcNMk4psaARzgMew+rjTsqZy9RMlGRB4XxWB2CSHrlD6yVv2PXpQsu999PbVIS7s88jW17EQyW5eeis9OfMQBOKU3hmQ3P0N61Pfe0vYfISSuRnANFQ9JVpLyunC0ZWxgeNByNSsO30d8y9+hc1o9fj06t4+to0e3/ctTLONk48cneT3hkzSP8MPwHqkxVHC06yoQ2E9A2OLrdyNi7ia+gPi0b79VB1M1WFoK968UduyJPSLg1MG6m0PR1Cbm4eU+HbxdxrIpcodlbniVKGC4lHu0g/xh0uvfSzqugoKCgcMOiBMFASW4Om36ZSXCnbkQOHUlZYTV7liVTmZNHD/cVeGXMBJUWuj4kMnbOgfDbg6TGlhKfG0T3O4KJGuhIzsdP4CUVU+IwkpyZJ5Cni4ynw7BheL/3LmonJwAqVqzAWjkbj5dexHXqVPJSkrAF7IKaApCs48cw+vmyLnUdA/0HolFpKK8rJ7k0GQ+9B38l/0VKWQopZSmsSF7BW73eYoxD96v6A40vjuejvR+xO3s3a1PX4u/gz6zYWQAklyaTXJZMXGEc07pN44H2ohbaoDXw4uYXGb+syXEupTSFB9s/iL+j/9V4G9cuzsHisST1/INgWRaNdg3Z5opc4QTXQMcJ4utyoFILg5H5E4W5iN4Nwm49937nw5C3RZ2wzv7SzqugoKCgcMMiybJ8xQ/arVs3ed++fVf8uKcjI+4Iiz56B1NNNbb2znh53ktBmQ2oVGisZZitGkL8Sgjs15WALiHYGkSGsqa0ml/e3I2Dqx0TXo1CrVUR9MpfeJsK+H73j6gK8lAbjdj37kXZmrVgNmPTujUgUxufgNrdDb8lS1g543+kRB/ARm/PuFffxj0gmOqKcmY+9TDpnbWs904g2CmYO0LuYPrB6c3WHuEWwffDvmfy6snEFsailtQsH7uc9PJ0jhUd4+EOD1+Rc2ixWnhzx5ssTVyKRtLgYudCXlUeEhKhzqHEF8c3ju3i0YXvhn2Hjdqmcdv+3P0sTVxKN89ubM3YysqUlaglNV8P+ZrePr2vyHu4LsiOhhn9YcJsaD/q/PY9/BssekS41g1/Hz4LF7W4Y7+5PGv9O9mHYUY/8fz2/0K3yVfmuAoKCgoKNz2SJO2XZbnb37ffVJngyt17KFu+DHNoJEnmYIqKZLS6LajUKnqMGc/uJb+TnrkTh6pigs3ZSIYMMpwHkJLXnxPzMpHmZ+IZ7IiDqx1lBdWYaq0MmxKOSiNRtW8f4eZihsWsg8J89j/yGndOGYWd0QnHffs4/sLzaCoqsGkdiv7221l97CB8+BYFaSl0HDyCw+tXMf+NaRhDgyhX1wCwxzGFYYHDiCuMawyA72l7D6HGUHKrcunr2xe9Vs8I3weILXwFi2zh4VWTyakS8lYqSUV0fjTldeVMi5pGG+fLY8X7c9zPLE1cysMdHmZS+CRcbF1IKklCq9Liae9J1zmigSnIMYjpg6Y3C4ABunp2paunGNPPtx8W2cK6tHU8ue5Jnoh8gpiCGFxsXXi3z7uXZf3XDU71mfHSjDOPMdeJuvXE9XDXT02Ndod/E487vxJNeRW5TaYoV4KG2mP3dtD5wSt3XAUFBQUFhTNwU2WCo0dOJMGmE7me3ZAl0TyjKvkJg4+BQQPHsuDX35Et2afsp7OxpUNEf6S6ALLy1NSZVWjUMp7eWtoc/5WqslL2VxVjlcBQYyLFwxuTVMfxTqMoC15Gp60SdvkmLAYtGhOokLDU1gHgEdSK8e9/xKszp6Dfn49LuQ6ADO9acoe6M+fWOeRX5TP32Fw6u3emv1//U9QURn+1nZj8I2gcjmDvkIubcxk5VUJb2Mfeh0pzJZ56T/6444/GBrULwWw1syd7D6tTV5NSmoIkScQXx1NWV0Y/3358Nfir087/4Z4PCXEKYVSrUdhqbFt0rEpTJa9sfYVN6Zsatw3yH8To0NEMChiEyWpCq7oJ6oZPRpbhAx/o+vCpurlFSZCxDzZ/DIVNmXcGvQFtb4Nv+ohynqTNUJYJ5hphwtFCLd2qOjN63UVcM8syrH9XZLAvRntYQUFBQUHhPDlTJvimCoKXPPQVmXbtCHUrJjyoll0rE0nTHUeldkdvN5g6lR1jHzbibDlEzvEjlP26mpp8LXE+bhQb7HAvq6Rjbil6WzssZWVgNiMD+0J9ybe3xSqrUElWHN09KaqtQK4sp1Zrxb5GQ6Xegl2NihqdhTTPKgod6/CqcaRP91vZY0hiY/pG6op6oSoPYGLEQGosDjw+sBWejnYsi87iu61JdPY3cnf3AP46nM2auBxUkoSNRkVKYSUmi8yUvsH8vi8dexsVnbutwNfBjZe7v8zi+MW8t+s9fr39V8JdhXuc2WpGo9KwOH4xf5z4g9bOQl840j2Ssroy2ru2p41zG+y19siyzOyjs5l1ZBbFtcVoJA1m2UywUzD2GnuOFB5h1ohZjdncS4VVtjL36Fx87H3Yl7uPNalryKvKI9I9kpiCGDq4dsDJxomk0iSe7/o8I4JGsC51HXlVedzb7gZtkPruFpEJnrxK1PQ22Bn/MBzSd4nnd/0kzCmOLGqyalZpmhzvfhgK1cVCnaIFsmQbjuUyedY+lj7dh45+5yFjpqCgoKCgcA1wUUGwJEmewB+yLPc7y5gfgPbAX7Isn9WC7GoFwbMemo+9nMHtfTayJtmDhGTh7uZiE4Ss6o+Xcx0jW3+DlHcEAKtZoqzNx0iuvhzLTmPXprWotVoiRt/BtoTF2B5IJsvFFp98F3a3LyLGw4UBbn2xc8lh14lNDDzkjarWhk1Ot5Jp54ubPhoHg0xGpR40pbj57qZCzgCrjtrC/tQVDAFAp1ZRZ7HiZKdFJUFxlanZ+9CpVXQLcsZskXE16FCrJJ4eFEpbL0dis0q5//vdONhqWf/iALRqFaW1pQz6bRBjQsfwRq83OJx/mEmrJtHK2IpjRccwaA1UmCqw09hRba5udiyVpEKv0VNhqqCfbz/uanMXfXz7UGWqwsnGCZWkotJUib328jckVZmqGLd0HJkVmdzifwvp5ekUVBegU+soqC4gyiuK3dm7AVh952p8DD4sjl/M2tS1QpPYJeyKrPOykncMfhohgliDFzywGKoK4JcxouSgx+PQ60kx1mKCfT8KzdzA3sJYAqAwEYpThDFHC/hgxVG+25LES8Pa8PQgxYxFQUFBQeH64oKDYEmSnIH5gIcsy13OMGYcMEqW5UmSJP0I/FuW5fjTjYWrEwRvXfcne39YjGwVga+EjIy4dT+8pzvtH/8aacFE9uTsIrbDHUR4d6e6upBkZ19qLbWYrCZMBaWYVh5BlVHWbO5SN3cWhEroXPY2bnup20vc3eZ+zFaZhQcyWBObS3JBJRq1xOhIH/LKa/l1XyJqfQrhLhFE+HjiYKtlar9gXPQ6TuSV89+1J9CoVAwIc+f9v45SWm0i3MeR+Y/2xNH2zKUA64/mMuXnffzv7k6M7iTUAKZtnsaqlFWoJTWWk1zC7mt3Hy91e4nC6kLc9e7EFMRQXFPM8qTl6FQ6vA3eFFYXMjhgMP38zngNdMWoqKvgePFxunh0aSy9KK8rZ2bMTP5M+BOLbKG0thSjjRGVpKKopqjZ/neH3c1rPV67qLKQq07Gfvh+0KnbJ61oudzaefDVxgQ+WX0crVri0JvDsLe5qVoJFBQUFBSucy4mCHYEJOBPWZYHnmHMdGCVLMsrJEm6G7CTZfmnv415FHgUICAgoGtqauoFvZELJWHLZv765icqNAUgyexx7ckRbTh/2PyHiFZuaHo8wv4lk5nk43nGOTQqDVqVlt7qjnQtCGPTwRyKtc6ke0ZSZJLRu+1hdDc9E9vdQYR7xCn7W6wyKonGAGx/ajEqCTr5G88ZlNWYLHy48hiPDQjB28nurGOtVpmh/91MYn4lLwxpw6TeQaCuYnH8YsrqyjBbzahVanwMPtzV+q7rOyA8CbPVjIzM2pS1LE1cipe9F6HGUAb4DWB71nb25+5nVcoqxrcZz5u93rzay7144v6EhPXC4MLOCH5Rwpb4EvPhymN8uzkRgGnDwxge7sUP25J5d3Q4WrXizKagoKCgcG1z0TXBkiRtOksQ/AMwXZblaEmShgFdZFn+8ExzXU2JtMkrnmZv/hZAMIuYKQAAIABJREFURoWWqFIt3aRy5jvaUIQZD70Hs4b/worjBymuUNHOpQ3+zkZCXB0x2oumtWXRWbz0ezSSBMuf6UeAix4ZGasV7HTXhlvViphs3lkWS25ZLQYbDbe09eDl4WH4u+iRZZk9yUVEBbmgUt0YAXBLMFvNvLDxBTZlbGJ40HCSSpMIcQohpzKHt3u9TajztW85fTX45x+HWbAvHQBfox0lVXVU1ln486k+RPorNcIKCgoKCtc2l1sirQJoSE8agGs2PfTPns/wU6wBg9bAwviF7HaqYjdqImoqibRzo4P/W9z15VFyyyRABo437mvUa/F0sOV4bjmdA4y8cXt7Qj3OYWl8lbg1wptbI7w5ml3G15sSWRadhYOthg/GRrA2LpdHZ+/n6VtCeWn4tWezfLnQqDQ82+VZdmbvZEfWDtSSulHDeOzSsQQ4BPBaj9fo49tUUrA3Zy/LEpcxImgEBp2BCLeIGyZz3lKKqupo6+XA+2M78Ogv+6msE+U0x3PLKa8x88WGePLKa1n9fH90GvGnX2e2Nj5XUFBQUFC4FrlUmeAHETXDn0qS9A5wXJbleWea61oxy5Blmc92zGb9uqNMCvBgxMjR9P8uAS9HW568JRSNSkKrViHLMqmFVaQUVpJWVEWXAGeeHhR6Xd0Kfun3aJYfzmJsZ192JBaSWliFk52Wg28MvamywSDMPVSSipSyFJYlLmN40HC2Z21nacJSMisymRA2gdLaUnKqcjhaeJSyuqYa8GGBw3ij5xsYba9uBnRvShHxuRXc2yPgsh/rrm92YKNVMXdqT/LKavhw5TEWHcxsfN1go6Gi1szsKd3p19qdrJJqen+4gY/v7MiEKMX1T0FBQUHh6nLJMsGSJLUH7pVl+fWTNi8BtkqS5AOMBHpe8EqvIJIk8WKfB6kpiePlrcl8npNMeY2ZOVMi6eDrdLWXd0mZNjyMrfH5rDqSQ6CrPf7OerYlFHAwvZiugS5Xe3lXlAad5WCnYJ7t8iwAYS5hjG41muc2PseC4wswaA24692pMFUwNWIqPb17cjj/MF8d+orYwlgWjlp4TqWJmPwYFhxfwED/gQwOGHxJM8gP/rCHapOF3q1cCXK7fIoXifkVJORX0K+1MNbwcLTls4mdsLfRsCe5iPt7BjAq0pce/17H878e4rkhrdmdLJoR18TlKkGwgoKCgsI1yyXTCa5XkRgKbJFlOedsY6+VTHADtWYLj/yyn7isMl4eHnbDfnDLstwYiJVWm+j70QaCXO3578ROuDvY4GR3k5lPtACTxYRW3XRedmfv5pE1jxDuGs5nAz/Dy96L3KpccipzyKvKI786n7yqPPKq8tidvZv86nwAnox8kic6PdFsblmWiS2MxUPvgYfe45xr2Z5QwL9XHmX+Iz3p8cF6quosTBsexlO3XJpa5opaM3ZaNer6OwOH0kt48IfdaNUqZj3cnQi/M18YPjZ7H6tjc5ttGx7uyYwHTrnwVlBQUFBQuKIoZhkKp7AmNodHZ+8HwM1gw6ZpAzHUy18VVtSi12la1Oi3N6WI6PQSooJcTtsodSi9hI9XHeOb+7ripNditlh5ZVEMR7PLMOq1hPs4MaSdJwEuejwdba75mtv1aet5fdvrWGUrthrbU2TYNCoNHnYeeNl78VyX51icsJglCUvo6NaRclM5twbfiozMgdwD7MrehYPOgb/G/oWzrfMpx/pqYwIHUosZ2t6TVxbFAPDCkDb8b/0JrDL4u9gxb2pP/F30F/WerFaZkNdWcFdXPz4dH4ksyzwx5wB7U4pY8lSfc86fUlDJj9uTuTsqgPi8cubuTqO6zsKyZ/pe1LoUFBQUFBQuFiUIVjgtn64+zpcbEwAIctUzMSqANXE5HEwroa2XA3Om9sDNYHPG/S1Wmb4fbSC7tAa1SuLHSVHE55ZzKL2EpPxKCitryS2rBWBoe09Kq0wcySqlqk7cyq+qsxCTWYrFKn4PvRxteW9MB4a2P7NU3bVASmkKM2NmAtDBrQN+Br/GjK7Rprnkndlq5s3tb7IsaVmzOXwNvnR078jK5JU8HP4w/+j2j2avy7JM1PvrKaqsxXqWP1OdWsXd3f0J93FkYJgHno4ts6Y+maLKOrq8txYQpTMrj2RzJLOMcV18+WxCp/Oe79VFh1kbl8v6ad2Ze3Quk8InoddeXKB+rVBdZ0GnUTVmzBUUFBQUrm2UIFjhjNSaLaw/msfMrUkcTCtp9pqv0Y7vHuzKy38cJirIBRd7HQfSivF31vP0oFDeXhrLyiM5/HNEWxYeyCAhrwIAbydb2no5UFlrIT6vvNH1roOvI5F+RroHuzQaeeSW1XA0u4zUwirm7k7lRG4F4zr78tnE8w++Ljcnl5RcCEcLj+Jq54qzrTNalSizeG3ra6xNXcv3gxaSVahlcDtPTBYra+JyeGFBNO+ODqeTvxFnvZDoSy6oxEajYuPxfL7dnMi4zr4sPpSJLIOjreb/27vv8Kiq9IHj3zOZ9N57hdB771WKFCuCZRH72suurljWtpafYndtWBCssKIoIIhIkSYQWkgggZCEhPTeJ5lyfn/cMGFMgNCSEM7neXhgJndmzryZM7z33HPew5NTunJ1v1Ac9c0v13cor4KJb/1hve3qYIezg56PZ/dr1pzxv04b+WjjEf5v1QH6D/qRQxU7eWHoi1zd6cpmt+dU1iTmkl1awy3Do8/L852JqloT3Z/9lVkDwnl1Rq8Wf31FURTlzKkkWGmW1IJKymqM9I3wJv5YKXcuirOO5IK2F4OXs701qXXQ63hwXEfuHdORYyU1XPPhFsZ3CbRJECwWSUWtCZPZgu8pRpVBK6316uokPtucxjd3DmZYB78L80bPwvrkfP6xeC8f/q0/Q2J8z9vzZpZnMn3ZdGRlX8oyrqVToDul1UbyK7S4//bIKGID3Rs9zmi2UF5jxNfNUVvAll/JBxuOsC+zlHAfZ26amE5SSTwTIycyNmIszvqTb7KyJaWQmz7dzktX9+DKPqHWaTHNUVZbxqwVs+ji04Xnhj5HbnUuO7MP8NLqbdj7r7Ye99uM3whyDWryOQxGM1/9eZR1SfnsySglzNuZmQPCuXV4FJ9tTiPS1wWzBXZnlPDZ5jSg4QSte8iFX8RaXFVHaXUd93+zhwM55TjqdSS/ePkFf11FURTl3KkkWDkrCVllXPn+FvzcHHhzZh96hnlyKLeCGR9tA2DDo2NsqhMYjGYc9bpzGi01GM2Me30DPm4O/HzfCAwmM7uPlvJLQg4lVXX8fXQHjhZVYZGSfZllXNMvlF5hZ1eybH1yPmFezk0mmaAl8G+tPYSdTvDN9gxrYvqfq3owe0jkWb/H42pNZnLLDDy/+U12li7BX9cfn+o5COlIgmER0cE1PDfqPgYFD2rW80kpWbonjceXr8Qlar71fr1wZlz4ZaSWH+T+vvczPmK8zeN+2pvFQ9/tZe0/RtExoOlY/FVuVS4f7P2AH1N+bHgdnR6TxWS93ctrNLsyCrH3SGSc3z28M/VeLBZJaY2RxOwy4o+VsS+zlF1HSyiqqiPG35XUgioAnO3tiPZz5UBOQ4k6O51gcLQPBRW1HM6vxEGv45ZhUfSL8OayrgHoL0DZwp/3ZfPwd3uwSO0kUEroHuLBygfPfBvxlakrya3K5ebuN1uvBCiKoigX1oXeLENpp3qEerLh0TH4ujng4qB9XHqHe3FZ1wBuGx7dqDyXk/2575jnZG/H45d34aHv9vJ/q5NYnZBLRnE1TvY6DEYLqxJsi498sz2DnmGeOOp15FfUMirWn39P62pNxLenFrH1SBEp+ZX0Dvfkqr6h7Eov4ZsdGWw6XAjAdf3DGNnJH383R3amFzO8ox9Gs4UPNxxh46EC62v1jfBiT0Yp/16WQKSPC6M6+Z/0fVgskqo6E26OegxGC9V1JnxcHWxOEF5YfoCvt2cAfYiINlHg9ANXDuzLrM6zmPD9FjINcPua25k3eh6ToyafMm615loeXv8wf2b/iUuUlohWHnqa0MByinRrWJOhzUl+eP3DXB/9D67scDVdgz2x0wkyi6sBmpz/XWwoZkHCAowWI1d2uJKuvl1JLEzk72v/To2xhomRE+ns05n+gf1Zl7GO9ZnrcdI70dWnK08Nfophr2zA7PE4qw6m4FC9j3VJ+RRV1VmfP9rPlZGxflzZJ5SxXQL4aW8Wrg563vn9MG6OemYOCKNHqCf9IrzpGOBm/YwVVdby9LIE5v+RCsAb1/VmdGd//m9VEruPlhDk6YS9nY5HJnSiT/2CTbNFcjCn3OZ5TsVskby88iA9Qz2ZPTSKGH9XvtmewZaUwtM+9rj9BfupqKtgXeY6FicvBsDdwZ2ZnWc2+zkURVGU80+NBCttkpSSK9/fQvyxMjyd7Xni8i5M6x3CpkMFJOVWMKl7EE72Ooxmyf/iMonPKsNikdQYzSRml/PIZZ3IKath+b5squrMCAEu9nbW3c4APJ3tqakz0zXYnfSiaspqjE22ZeaAMJ64vCtpRVX0i/CmqtbEtR9u5VBeBe5O9oyM9SPY04nKWhPlBhOVBhOVtSZSCyopqTbiYKejzmwBYGK3QO4eo41kJ+VW8MkfqYyM9adToBsTugXxZeqzbDi2wfracwfNZXXaauIL45kROwM7nR2PDngUBzttfrDZYmZF6gqWHl7Knvw9AFzf+XoSC47gJjszLXI2V/QOYf4fh5m39SukyRW3gO2YHQ9RWzgGT8M0fF2dScqtwM7tAFGdV+Pl6IWfsx8BLgFUGatIKU0hrUybguBm78aS6Uu4aeVNlNSW8P749xkVNuqUv8vp720mzfUfGMv6IIqvZnzXQPqGe9ElyIOeoZ54ujR/RDSxMBFnvTMxXjHW+46VVDPi1fVM6RmExQKrE7WTpL4RXhwtqsYiJRO7BeLmaM+BnDL+TNWqedw4OIIIHxduHhppPcGrrDXxwfoUgr2cOVpYxc6jJezLLOW9G/oyvXcIAK/8cpAFW9NJ/s/kJq94VBmrcLJzwmA28Nn+z6wLKE8U7h7Om2PeZFv2Nm7pfkubr4iitE0Gk4EiQxGhbtr6irLaMspqy4jwiCCtLI07fr2DTyZ9QoxnzGmeSbMnfw8dvTri7tC8q0GKcrFQ0yGUi86hvAq++vMoD46PPWWFihNZLJKZH28j7mgJTvY6RnT0p2+EF7cMi8LZ3o41B3L5fEs6Nw+NZHL3IOvlc7NFsu9YKUfyK+kU6E5RVS0VBhMRPi70CvNqVAkgr9zAl9uOsnT3MXLKDLg42OHmqMfNSY97/d/Bns6EejlTa7Lg6WxPemEVi+Myrc9hpxOMjPXjtRm9CHDXKjpUG6v5I+sP9uXvo5N3J66OvZpqYzX3/X4fcXlan7m+8/Xc0fMOXvjzBbZmbcUkTcR4xhDqFkqoWyhPDn6yyaSqus7ED7uzWJ+cS4LpLar1idjjTjS3EuAcSJLlQ3R2dXTz6UZ+TT6Hig8R7BaMt5M3N3S5gd5+vZmxfAbVJm3U+IlBT3Bj1xtP+ztZnZDD83vm0NO/K2+NfQPXU8w3rqiroNZci5+z7VzwzIpMnt/6PNtzt+Pr5MuyK5fZ7Np316I41hzQ6hRH+LjwzvV96Bvhzd7MUp5fnkhumYFKgwlT/YkSNOx0N61XMG/N6oO9nY4PNqTw2mptq3QHvY6eoZ6MjPXjvrENO0R+vPEIr6xKIuH5STZzp41mI+/teY9FBxYhkVikxfqzh/o9xLSYaRwqOYSz3pk71txh/fmSaUvo6tv1tHFU2j+LtJBfnU9mRSaZFZk42TlxefTlJz1JemLTE6xIXcGqa1ZRWlvKE5ueIL08nc8nfU5cXhwf7P2AKzpcwUsjXgKgzlzHspRlTIqahKej7Vz6KmMVQ74ZQifvTiy9YukFf6+K0pJUEqxcMvLKDWxMLmBS96AzGmU8G7llBmqMZqKbsWubyWzhyR/30y3YgxGx/oT7ODe7goPZYiarMouP4z/m5yM/AyAQTO8wnVFho5gQOQGdaP582NyqXJYfWc6ao2tIKk4CtPm8r496nfGR462veXx3veOWHlrKc9uew07YsfOmnTYVIU5lzqo52Ons+HzS5zb3Vxmr2Fewj115u/g1/VeOlh8F4NYet9IvoB8lhhK+TfqWzIpMBIIZnWfw5YEvMVlMeDl6MS1mGrf1uI2KKme2Himic5A7/SO8T7oVuJSSOrOFlPxKuod48uGGI7y6OolxXQL46G/9GfnaOkxmyUtX92RclwAc9I1j+r+4TB77Pp7v7hxMlakcL48KOvt2YH78fBYkLkAv9Fzf5Xoc7Rz5LOEzAOJvjrdJZD7d/ynv7H7HenvNtWsIdgs+ZQzf2vUWqaWpvDrq1XZTbu5StjtvN2sz1jKn2xw+2f8JO3N3cqziGHWWOpvjhgQP4YG+D9DL37YaSXZlNpOWTgIgzC2MrMosdEKHWZq5qetNmCwmFicvpqtPV5ZMXwI09F+AXv69mDtwLj39ewKwLy+ev62+CYAHur9IRXEXNqfk8cZ1/Yjxd7uQoVCUC04lwYrSTiQXJ7MydSW9/XtbE9azZTAZeCPuDUpqS/hn/3+eNhGTUrIwcSEDgwfS3bd7s1/nkfWPkFaWxrKrllFWW8bH8R8TlxtHckkyFmlBIBgQNIChwUPZlrONnbk7bR7fzbcb/+z/TwYFD2J/wX7mxc3DbDFzoPgA/QP68+mkT8/q/QPW0d9oP1fSCqtYeNsgRp9irvf7O37g3c2/o3c7gM7Rdm5wjNNIFl7xOl7OWpK6/MhyqoxVXN/lepvjLNLCwsSFrElfQ0JRAv8e8m9mdp55yhJ8PRdqycpLI17iig5XnLR9deY67HX2aopFG2S2mHlo/UOklaWRUZFh87PhocOJ9Yol3D2cMPcwwt3DeWf3O/ya/it6oWdC1ATc7N14fNDjlBpKuXvt3eRU5TAlegrfH/qeWZ1n8VC/h7h77d0YTAZMFhNHyo7gau/Kthu2sSFzAw+u17aJ/1vXv/H9oe+5suOVPD3kaUxmC9d99zIppsVIiwNCpyXiZkMwIVX/4tVr+5FfbuD3g/ncNiKGbiEeLR679iS7Mptg12DVR1uQSoIVRWk1L/75IitTV/LeuPd4fNPjFNYUMjBwIH0D+9LXvy+9/Hvh5qCNNpXVlrE+cz0R7hG42rsS4hZy0jmKx0dUZ3SawaMDHqXGVMPqtNV4O3kzMWpisyowVNaaGDNvPd4uDtw3tiNX9Q1t8rhVaav4dP+nHCo5BEC4c3ecTb05UHgYnUsy5qJJGEr6MWtABK/O6MXWI4V4OTucMmGQUjJxqdbOO3rewetxr3N/n/sbTTMxmAwM/HogoCUwjw96vMnn25u/lzmr56BDRy//XswbPa9ZW3Ir5ya/Oh9Xe1dc7RtfEaqoq2BD5gamxkzlQNEBblh5A/0C+jEhcgKdfTrz2f7P8HHy4aURLzVKigwmA/Pj5/PJ/k8IcA4gvyafPv59yK3Opby2nHfHvcugoEEUG4rxddbKNr7454ssTl6MXqcnxjOGQyWHmD9hPvf9fh9BrkHc0+tB8nI68WHyowR52fHW+Jd45LeXyKjZDcD6GVt5+NfX2VfxAwCybCiVuZNxDvkWnVM2lvIB9PQaj96+nOemDqOTT/PmGzdFSsmmrE0sTFxIeV05+dX5dPDqwNUdr2Z95nrGho9leofpTT5uWcoyxoSPaXKnzbZsQ+YGHlj3AOPCx/FQ/4eaPV9bOTcqCVYUpdUsP7KcJzc/ib3OnhC3EJ4f9jz9A/uf8/OW1ZYxY/kMcqtyuTb2WrZkbyG3SlsYF+QaRLRHNCPDRnJZxGUEuwUjpWTjsY308OthM++41mTGwe7kpf1+O/obj258lGDXYGZ2nsm1sdda51QeLaqisLKOnqGevLY6ic+2pDFnaBRfbE0HYNO/xhLo4cSWlEK+332M7NIaXrmmJ6XVRuLSi8mo2cN+w5ccq0y3vt6MTjPYkbMDk8XErT1uBeCl7dq8zgGBA1gweUGT7Xxq81P8nvE7MzvPZHHSYrwcvXhz7Jt09OqIo13z5tUrtqSUZFdl4+vki5PeiYLqAusoaohbCIdKDjHj5xnohI6pMVP5e6+/82fOn9jr7Lmq41W8vP1lvkv+jrmD5lJtrObdPe+yfub6RvPeT6XOXIeDnQMLEhbw5q438Xf2573x7zV5NSajPIO1GWuZFjONpOIk7vv9Pu19mNypSn0IN70XFbUmnAJ/wd5H2yBHml0IshvCIyMnMjVmKgBGi5GnNj/FqrRV2Al7zPKEhcNSgNByhx037WiyBrmUkrzqPJYkL6GLTxcGBA0gLjeOwcGD8XT0JL86n4/2fcT/Dv0PDwcPyuu0Uoh+zn4U1mhXWDp4dmDZVcsaPXd8QTw3/XITl0dfzmujXsNoMfJb+m846h0pMZRQUFNAQXUBdeY6ro69+rx815xKZkUmy1KWcUOXG077e73j1zvYnrsde509JouJydGTubHLjWzJ3sKszrOoMdYQ6BpoXfysnB8qCVYUpdXkVeVx2feXEeYWxheTvyDQ9fxti11ZV8nUH6dSbCgmwCWAt8a8xeGSw2w4toGDRQfJq9YWzA0KGkSoW6i1rvG0mGk82PdBakw1OOudCXYL5mj5Ud7a9RaltaWMDhvNjV1vZHXaap7b9hzdfbszf8L8U87Hza8wMPSVdZgtki5B7iTlVgDg7qinotZ00sf5uTvwwiw9XybNJ6EoATd7N/oH9mdH7g5qTDXW4/r49+FY5THWz1wPQFxuHEnFSeRU5bAkeQkGs4FZnWfx9JCnSSxK5M41d1JRp7XBw8GDYSHDmDd6HqCVvlt+ZDlXdLiiWaNpF9s0i3PZ3XFv/l5c7V15I+4NSmtLSSxKZELkBN4c8yYPr3+Y3zN+J8YzhmEhw0gvT2dz1mYmRE5gc9Zmm9/XickdaPP4+wb0ZeHlC8/6feVU5uDh6NHkqPOK+Gy+/jODJ6d0pWeYJ7lVuUz4fgIA1Zk3Y67sxtSewdw2IpoFO/5kY/W/AJA5d/LbPXcS7GmbzJosJnbk7GBr9laOlh/lP8P/Q4Wxgu+SvmPRgUUATI+8kQGhnUkpTcFF70J33+6EuIVw/7r7rSekJ5oQOYEg1yC+PPAlALM6z+LxQY+zLGUZng6ejIsYR1xeHDtydvDJ/k94bMBjTO8wnQ2ZGzhYfBAXvQsJhQlsz93OwKCBvDDsBd7a9RZrjq45acyGBg9lYtRErup4FTtyd1BUU8Tk6MknvVJUa67lYNFBjBYj/QL6NVobcaJqYzV3rrmT+MJ47IQdHbw6UFFXoZWHDBlGXF4c+wv3c12n69iQuYFntj7DowMeZXqH6XyR8AULEhtOaINdg8mpymFM+BieGvxUo82FpJSU1pbiaOeIi70LBpOBLxK/oNZcyzUdryHcI/yk7bzUqSRYUZRWtTV7K7Fesfi7nHy+7dlakryEdZnreGHYCzaX/4tqitiSvYWsyiyWH1lOZkUm0Z7RdPDswKasTdSatc1PPBw8WD9zPbNXzeZA0QEiPSI5Wn6UYNdgKuoqiPGM4YPLPmi0or4p8cdK+TO1iNuGR3PHojg2JBdwZZ8QrugdQv9Ib/ZnlbEjrZguQR4MjPImu8zAzI+3MbKjHx/O7k1GeQZRnlHodXoSChPYeGwjo8NG4+ngyY8pP/JZwmfs/ttuTNLEgK+073S90FunlLw84mVrOwtrCtl0bBPZlXn8cOAP8k37WTtjLUaLkZkrZlJRV8GosFGYjM5Eukczd9g9TS6w/PHwj7yy4xXGRYzjlRGvnHFyWWooZUXqCiZGTTwv0zOklOwr2EdPv56NEpTyunI+if+E5UeW8/bYtwl3D7dOFWjO8z695Wnr4tO/enfsuzy4/kE8HDzwcfKhoKaAKmMVI0NH8sFlH7Azdycfx3/MzE4z+T3jd35J+wWA98e/T2ltKb+l/8b9fe+ns0/ncwvASVz25kZS8iu5b2wH7hvbkWd/SuSHgxvw86pi1e2P4uJgZ62PnVlczeh3vkbYl3Jzryk8e0Xz5/gDJOeWc/VPM7BzygG0hbUWabGpigLwyshXCHIJYsmhJeRW5bInfw8CwaSoSVwdezVDgoc0+ZmrMdUw94+5rMtcZ3O/XqfHWe9sPbkD0AkdU6KncFnkZXT37Y6vky951XlUm6rZmrWVj+M/ptJYiau9K1VGbTOek00rKjYUM2fVHNLL0wEIdAnkuWHPMSJ0RKNjLdLCA+seYHPWZu7qdRcWaeGnlJ+sJ94uehdrNZ3jBgcP5sPxH1oXFS9MXIiDnQMdvTryrz/+ZR0FFwju6nUXs7vNJi4vji1ZW9iStYXsqmwAAlwCqDHWUGFsiEOkRyRzB81tsq0twWg2Mi9uHpOiJl3w0fczpZJgRVEuaVJK0srTiPKIQid05Fbl8tWBr9hXsI+9BXsZHDSY7bnbeXrw08zqMos/c/7kzbg3SSpOYtHli+gT0OeMX7Omvi61s8Opq4As2JLG88sP8Nikztw7psNJk8xvk77l5e0vs37mejZmbuS5bc9xS/dbmN1tdqPkMru0hseXxhPu44KLvR2f79yCa8y7gLYIa0vWFnr49CWheA9S6hDCwpXh9/LiuHvYnLWZ13e+zvQO09mdc4A/chpG2RZPW8y27G242bsxq8us08bAYDIwd9Ncfs/4HQ8HD0aFjSLYNZgQtxBCXEMIdgvGaDFa57A2x5r0Nfxz4z/xcfJhSvQU7up1l3U0+561WvuPE4hT/v6klBQbikkuSUYv9Ny+5nYAQlxD+PfQf3PP2nsaPWbeqHlMjtY2r6k2VuNo59goGTdZTPxw+AdGh40+r1c+/upIQSXBnk7sySjlpk+3AzC1ZzDdQjyY96tW7u+uUTE8OaVxGb67v9zF6sRc3r+xH1N7nXpR7F9JKRnwyo90Cq3jjWtH4e/sj0m5xiP7AAAgAElEQVSaSC5OJrEoEV9nX8aFj7O5rF9YU8jW7K0MDR7arJNhi7TwecLn1koqJ9YlX5m6kk/3f8rUmKlcHn25tVZyU8wWM18e+JLtuduZGDmR7bnbWZW2iscGPMZVHa/iqc1PsSt/F0EuQWRUZGCRFm7tcSshriF8sv8T9Do9P135U6N++fG+j/nv3v/y1OCnrItfa821JBcnszBxIZ6OnowKG0VmRSbzds5jeOhw3hrzFk56pybbWVhTyLbsbezK20WlsZJf03+1/szV3pUhwUOorKtke672e54WM42rO16NSZpIL0vn26RvKa0tZdHli4j2jD5tfE9FSklCYQJh7mHNnnd9vF86652ZP2E+fQL6UGuupcRQgq+zL0lFSdZqJC1NJcGKoihNqDPXccWyKzCajfQP6s9zQ5+zTnk4Xrf1r5clzzcpJbcvjGNdUj4A/57WjdtHaP+Jzf/jCD1CPBnW0Y+1R9fyyIZHWDxtMU9segIHOwfeGbmQQA9ndALrf9IlVXWMeX3DXzaAseAY9BNRoblkVx+li09XCg/fSqHhGLcOHMGClGeRzsnc0OF+9pdu4kBxPBIzUtpRVzgGU0VPXGPexkG4Uie10bRH+z3FnJ62lS9OtOnYJl7e/jLHKo8xJXoKxYZi0svTya/ObzRi6Kx35rcZvzVrtP25rc+x9PBShocMZ3vOdqbETGFO9zmklqby2B+PAXB377uJ9erEk5ueIcgpmqXXLGg0L9psMfPEpidYlb7K5v7fr/vdelKRVpbGvoJ9BLoE4mLvotXTPcnoZUtbuusY//zfPuvtCB8XAj0cOVJQRXWdid5hXgzr4MffhkTg20St9Yyiat5ee4iXru552hO1pjzzUwKLth3l2enduHX4uSVdp1JWW9asz0VzVRureXTjo2zK2oSz3tk6haWXfy96+vVkctRk60nTj4d/5Jmtz/D2mLcZHzme3KpcNmZuRAjBi3++yNSYqbw84uXTXh3JrcrFz9kPva75G/WuTl/N4ZLDDAkeQp+APtjr7JFSYpZmpJSNSlSmlaVxy+pbcNY7MzFyIm4Oblwbey2f7v+U8rpyRoWNIqEwgazKLKbGTGV8hG11oeTiZFakrsBeZ8/W7K0204BOZn/Bfj6O/5hY71h+OPwDznpn7IQdJYYSXhrxEi9uf5H86nzr8SuvXkmER0SzY3C+qCRYURTlJM5l/uj5UmsyM/SVdRTXbyk9tWcwozr58fjS/eh1gjdm9iYypIDZq2YzNGgk23I3EVR7K4dTO+Pr6kCN0cycYVH8Y0In3l57iPfXH+HTmwcwIMqbvPJaXBzsmPLuJkbG+jFpYCmPfJ2OpTaIT24ewIRugexIz+GW1bdj56xt6FJXNApjaX8mdQ3jxemjWRmfzZuHbsIka6nLmYWd53bsXFK5t+On/H14P+pMFioMRgI8tFGu5Yc28uS2+3GUQYSab2JKx5HMGhiOl4sDRouRguoCsiuzyanKoay2jFd3vspD/R7ijp53NBmfzIpMUktTGRg0kCt/upIu3l14b/x7vBH3Bl8kfmFz7OujX2dS1CSW78vmH6s+xCl4GXf3fJD7+t1JVmUWT256kjnd5/Bd0ndsy9lGqFsoA4MGEuASgL3Onrt7333hftHnya6jJdww/0/qzBZ6hnoytVcw1/UP4711KXyxNZ0Bkd58NLt/szcaOhtmi+Ser3ax9mAeX9w66JTbyLc1Uko2Z21m0YFFBLgE8OLwF5veZMhYzeBvBgNwRYcr+O3ob9akOdY7lq+nfN3kwsDWsitvF09seoJiQ7F1utdf+Tj5UGwoZkLkBG7vcTvd/bpjtpiZtHSSdSpHpEckvk6+7M7fzbWx1+Lt5E0n705cHn05oK3FWJy8mP/u/S92wg6jxUiIawgfXvYhjnaOzFk9h5yqHPQ6PQ/2fZCKugo6eXdiXMS4Vln0p5JgRVGUNm5vZikLtqRhMktW7s+x3t8n3Iu9maVcOcCZdVUPAWCq6Io5ZzYzB0aRW2Zg7cF8m+caGevHotsG2fzH/urqJD7ccMT6fM72diQ+P8m6ucjjS3ezNGE3YT6Cyzr057p+Hegc1FCeLqk4iV/3l/DR2jIGdrSwRz6BxeSOr2UUVfmjKKo08/s/RrFg/2KWZbyPNLnRyfQfamp11kWCXYLcmdIzmCk9g+kY0LAJw51r7uRI6RF+vupn3BzckFKyK28XK1JXMCpsFB/t+4iDxQetx7879l3GRoyl1lzLdcuvI60sjXmj5tHJuxPRntEIIbhj4U7WHszHOWI+3p4VbLrhN57b9hzLUrSKA56OntzT+x5u7HJjq58EnYntqUXcsTAOb1cHfr5/OF4uDUlFemEVaw7kMmdYVLM34zkXx7eRzys3sOOpy6w7K55o7tJ4zBbJvOt6X/D2XAi/pP7C45u0+cOjwkZxQ5cb2J6znTnd55xRlY+WtiJ1Be/veZ8qYxXzJ85nV94uJkdNxsPRg8/3f85H+z7CLM0EuAQgkeRX5/PaqNfoG9AXf2d/UstSeXrL02RWZFJtrMYiLbw++nVSSlP4+uDXlNeV09WnK/8Y8A96+vXE0c7ROtKdUZ7B+3vfZ0DQAK7rdF0rR0IlwYqiKBcNk9nCivgc1h7MY1Qnf67oHcKD3+7ht4M5TBuVwC+79Dwy7CruGdPRuqV3Za2JKe9sQq8TPDmlK+O6BDTaOc9gNDN3aTzL9mYT5u3MigdG2CRQUkqMZtnkTnknMlskdjrBpmObeGP7Jxyp3IMh+1pMlV1wD/sR6XIAqjvx7yHPMLOvlvj8mphLYlYZ21KLiDtagpQNCXH/SG8qSWHu1nsYFjqMd8e+y9cHv2ZenFbJwk7YYZZmZnebTa2pljD3MOZ0n2OdklBlrOJA0QEGBg20trG0uo6BL63llmFRbMpeT5bDR9zY5UZWpK6wVmzYfP3m83qZ/UKqqTOzaFs6yXkVrIjPIdzbmS9vH0yIV+uPQq6Iz+b+b/bw033D6R3uZfOzNYm53PXlLkCbm/zwZbG4ODR/SkBbkVaWRk5VDkODh15UJ0ynUlhTyNxNcymqKaKrT1ec9E48PujxJsspFlQXMOWHKRjMBgDGhY/jrl530d3vzBZUthaVBCuKolzEsktrGPHqOiwSAtwd2fDYmEbJRFWtCQe9rsnRuOOklCzemUmIl/N5uXwtpeS65bNILqkfpZX2GPImseCaRxgR23QliNwyA6sScvhlf441IfZzc+CxGcW8uP1F7u19Lztyd1BeV07fgL4sTl4MnFnS+t2ODOb+sJ/l94/gz7QC3tj7DA4eCUgks8Kf5e4h46moduDXxFwmdgu8YFsDb0kp5JVVB3n/xn5E+p5+e/UTpeRX8tnmVAxGCzvTizlWUoOdTtA33IuPZ/dvcp5va8gvNzDo5d+5YVA4L1zZw/r5qzAYmfDmH+gEZJdpydM9Yzrw+OQurdlc5SyllqVyqPgQvfx7EeIW0trNOSMnS4IvvtMxRVGUS1CIlzPTeoXw875snpjSpcnRNFfH03+lCyG4ftD5W5gihOD5Yc/ywLoHGBk2kms73khFuS/DO578MnGQpxO3Do/m1uHR5JUbeH99Cou2HeXyqGv49eivfLDvA0ArYzU+YjyLkxfT069nsxJgi0VikZL/7TpGtJ8rPUI9MFksGFbexHBvB1JKjvDpGme2JSRwOL+SOpOFBVvSWP7ACALcnRo9V53ZQoXBhL/76RPOOpMFiaS02sj2tGKSc8tZuPUolbUmJr71B8M7+lFWYyTS14Unp3RtNF+3qLKWQ3mVpORXcCivkmV7s6gwaPWlO/i78t1dQ+gf6X3Kk5zWEODhxKwB4Xy7I5N9mWW8NqMXPUI9+XxzOrnlBn64dxi+rg48sngvqxNyCfd2Ib/CwOhO/rg66lm0LZ1+Ed5c0y+syeeXUmKRWK96HGcwmrG30zW6H7TfnaTxY5SzF+MZc9Y73JnMFux0os2NoquRYEVRlIuEwWimstZ0QRc7na1zWVx4/HL66odHsizjA74++DUACycvpF9gP1JKUnBzcGtWlY7nfk607tZ3vMpGncnCtPc2caykBns7nbVqxsRugdwwKIJ7v96NnU4wtIMvPUM92Z5WhKuDnsTscrJKtUVQGx4dQ5Rf0yO5KfkVvL32ML8dyKPWZFv1ws1Rz2OTOpNaUMnK/TmU1RgxWyR6nY4J3QN54Yru/N+qJNYl5VNUvygStA1W+kZ689JVPThWUkO/SK8WmeN7LlYn5PDvnxIprqqjR6gniVlljIz1Y8GtgwD4fHMaL6w40OhxQoCUMGtAOFf0CSHS14WDORVE+LjQOcid11Yn8fEfqXQOdOfuMR3oHOjO19uP8vX2DBzsdAR4OBLg7sjEbkGEeDmzKiGHFfE5dA/x4I2ZvekSpG1dLqXkSEElqQVVONnbobcT9Arzwq0ZJ4/KuVkRn80zPyWy9J5hRJ+kH11IajqEoiiK0ibtOlrCtR9uZcEtAzlm+ZXXdr7G5VGX89ro187oeQ7nVTDx7T+QEubN6MWM/mFNJuZPL9tPQlY5S+8Zhp1OsDezlCVxmfxxqIBjJQ07vvm4OlirdXQP8WBErB+xAe6EeTuzJaUQR70OFwc93+zIIK/cwLRewXi5OOBgp2NclwA6B7ljpxPWkVuzRSLQavt+uyOTL/9Mx2jW/g+e2C2QQdE+dAp0JzbQjSAPpzY3atYcZdVG/m91Egdyyhka48ttI6KsI+xSSgoqa7FYwEGvY+HWdJwd7Li2XxifbErls81pmC0nz0k8ne1tyv5d0zcUTxd70gqrSMwup6BCq4bg6mBHVX2NbgBnezu6BLuTX15rPak5Tq8TBLg7YmcnmDejN0NimrexyrlIK6wiObeccB8XYvzczqo8XXNYLLLRuoDW8uxPCSyJO0b8cxNb5UqGSoIVRVGUNim3zMCQV37nxat64OG/j6c2P8Vdve7igb4PEJdeTJCnE3nlBlLyK5k5ILxRcpheWMVbaw+xJjEPNyc9y+4bTuhpFow1NXJ9PEnLKKom2MuZUC9nSqvr+ONwIU/9uN86NaEpZ7PhxMr4HO77ZjcAB1+YfMGSoYtFSVUdB3PKOVpcTXFVHRaLxGSR6HWCGwZH4Olsz56MUhKzyxjfJZAI34YtzC0WyeK4TCJ9XBgc44tOQEm1kZ/3ZrEro5T4Y6UEezoxvXcIPUM9KasxYjBa2HW0hKzSGvZklFBUWce/Jnfm5qFRZz2NQkpJQlY5gZ6O+Lg4UFVrxtNFq+dbbjCyM62YJ3/cT165lrAHuDuy+uFR+LieedmwmjozB3PL6Rvu1eiznJhdxpzPd/DU1K5c3bfpaSYtRUrJ5e9sws/Nka/uGNwqbVBJsKIoitImmS2SHs/+yswBYTwwPpofjizh5u43kJJXy7T3NuPupCfc24UDOeXcNDiChy6LZe7S/Ww8VGB9PICXiz3f3z2UjgHup3q5s1ZnsrAqIYeU/Equ7RdGiJcz1XUmjGbZrDnDTamsNVFUWXvGi+aU8yu/3MBj38ez8VAB4T7OTOwWxN9HxVjrXp+MxSJ5ZMle4tJL6BfpjUVKVsZr5Q0d9DrqTBZCvZzxcrEnJb+SWpMFBzsdr83ohdFs4bHv4wEY1yWA6b2D6RrsQQd/N+ztdEipnQT8deT0YE45S3cd43+7jlFWY2RQlA9TewUzvXcIPq4O5FcYuOq/W8guMxAb4MbyB0agE+K0VV8ulEXb0nnmp0T+c2V3Zg+NapU2qCRYURRFabNmf7ad/Vll1BotmKXk5at78ummVGt9YdBKqp14e1qvYKJ8XdEJ6BTkTt8I79OOACvKyUgpWRGfw9trD3GkoIoO/q58PLt/kydVUko2HCrgPysOkFqg7aB4POm9tl8Y3vWjv8VVdRRU1lJrtNAl2J3J3YPoHORurezx875sdqQVsT6pwDpVY3yXAK4fFMG8X5M4lFeJr6sDgR5OBHk6YTRb2HS4EL1OMKlHEJ7O9mxPLeJIQRV2OkGYtzMGo5myGiN3jYzh3XUp1ja7OtjRMdCdHiEedA/x5PIeQXifxQj0qeSWGSisrKV7iAdCCLYdKeLmz7czKtafT24e0GrTM1QSrCiKorRZxxdNBXo4Wi8VA3x68wB+O5BHfoWBt2f15Y/DBRzIKSfaz5Vr+oaib2OVEpSLn8Ui2Xi4gEeX7KO0fqT1ugFhTOsVgp1OUF1n4u6vdrElpQh3Jz09Qjz56G/9cXawIzG7jN5hXmec7Fkskh3pxXy3I4Nle7MBiPJ1YXrvEAor68gvN5BbbqDCYOK6/mHcMDjCZoFsUm45P+/NJrWgCoPJzD2jOzA4xpdle7JYn5xPlK8r5QYjmw8Xkl9RS1mNEb1OEBvozuD6ueiVtUaqas042duxJ6OEg7nlTO0ZQpi3M+5Oegor66gwGNmQXICDnQ43Jz2xgW7cOiyaIwWVPPTdXgortb7r5+ZAnclCucFEpK8LP983wjotpDWoJFhRFEVps8wWSX6FgWBPZ9Yn53Prgp10DHBj7T9Gt3bTlEtUXrmBF5Yf4NfEXEwWaa1icdx9Yzvw0PhO532awU97szCZJdN7h1yQKQxSShKzy1ken82B7HK2HilqtCDRvb5iRkVt43nw3YI98HDWU1pttLkyA3DnyGii/dxYcyAXgL7h3tw2Igp3p9ZLgEElwYqiKMpFwmyR/HddCjMGhKnpDUqrMxjNrErIYW9GKXo7HUeLqhnVyY8bB0W0iysRxVV11JksuDnpta3Us8vo4O+Gq6MeKSXHSmowmi24OurRCWEz/31vZim7j5bg6+bAkBhfAk8zh7q1nFMSLIT4DOgGrJRSvtjEz/VAav0fgAeklPtP9nwqCVYURVEURVFawsmS4NOewgghrgHspJRDgRghRGwTh/UCvpVSjqn/c9IEWFEURVEURVFaW3PG8ccAS+r/vQYY0cQxQ4BpQogdQojP6keGFUVRFEVRFKVNak4S7Apk1f+7GAhs4pidwGVSykGAPTDlrwcIIe4SQsQJIeIKCgrOtr2KoiiKoiiKcs6akwRXAsdXJrid5DHxUsqc+n/HAY2mTEgp50spB0gpB/j7+59VYxVFURRFURTlfGhOEryLhikQvYH0Jo75UgjRWwhhB1wF7Ds/zVMURVEURVGU8685SfAyYLYQ4k1gJpAohPhrhYgXgC+BvcA2KeXa89tMRVEURVEURTl/TruATUpZLoQYA0wAXpNS5vKXkV4pZQJahQhFURRFURRFafOaVcVBSllCQ4UIRVEURVEURbmoXfxbnSiKoiiKoijKGVJJsKIoiqIoinLJUUmwoiiKoiiKcskRUsqWf1EhCoCjLf7C4AcUtsLrtjUqDo2pmNhS8WigYmFLxcOWikcDFYvGVExstVY8IqWUjTapaJUkuLUIIeKklANaux2tTcWhMRUTWyoeDVQsbKl42FLxaKBi0ZiKia22Fg81HUJRFEVRFEW55KgkWFEURVEURbnkXGpJ8PzWbkAboeLQmIqJLRWPBioWtlQ8bKl4NFCxaEzFxFabisclNSdYURRFURRFUeDSGwlWFEVRFEVRFJUEK4qiKIqiKJcelQQriqIoiqIol5x2mQQLIURrt6EtEEKMEEJ4tHY72hIhhG9rt6GtUf1Fo/qLLdVXGlN9RaP6SmOqvzR2MfSXdpMECyHshRAfCCH08hJf7SeEGC2E+AG4BRD197X5D+OFJIQYLIRYArwjhJgkhHBt7Ta1JtVfGqj+Ykv1FVuqrzRQfaUx1V9sXWz9pd0kwYArMBW4C0AI0Z7e25l6AlgnpbwDiAa4GD6MF9htwHfAV8AVQKfWbU6rU/2lgeovtlRfsaX6SgPVVxpT/cXWRdVf2nTjTkcIcZ0Q4vj2e5HAImCGECJcSmm5lM5Q62MxuP7m+8AEIcR24DUhxGNCiM71x11qMekvhAgAXKWUP0gpVwMhQGj9MZdaPFR/QfWXv1J9xZbqKw1UX2lM9RdbF3N/uSjrBAsh3IDPgCK0s47FQIKUMkMIcS/QQ0p5b2u2saU0EYuvAW+gD/AmoEc7U82WUi5orXa2pBNiUgy4oBXnfgvtc3IQuApIklK+2WqNbEGqvzRQ/cWW6iu2VF9poPpKY6q/2GoP/eViHQmuASzA48CLQBgwHkBK+QEQLoQYDm1/KP48ODEWL6OdiQYCb0gpC6SUOWi/53C4ZM5Oj8fkX8D/ocXkFyAPcEf7vBTBJfH5ANVfTqT6iy3VV2ypvtJA9ZXGVH+xddH3lzbZqGbwR/vQdQSOAPuBSCFE3/qff4zWaZFSWlqlhS3neCxigcNAMtrZeuf6SfqBQAxQApfM/K0TY5IMZAFmYKeUcjHwLVpnvRQ+H6D6y4lUf7Gl+oot1VcaqL7SmOovti76/tLmk2AhxPGzTPvj90kpc9FWpnYH7NA+jOVAVP3PVwD/FfVaus0Xymli0Q0tFofQzkRjgeFonbIC+KCl29sSmhmTZKC6/jaAL7CuZVvacoQQOiGE3fHbl2p/gVPG4lLtL6eLh+orqq8Aqq+A6i9/Vf+xt+aN7aG/6Fu7AScjhJgAPALsFEI8L6U01t8/FW3l4WFgCHBMSrlBCBEB1B1/vJTyf63Q7AviLGLRGUiUUj4jhHCRUla3WuMvkLOISThgqn/4h1LK2tZo94VSH49ItHl7huNn3ZdwfzmTWLTr/nIW8WjvfWUM2mXsH1F9ZQxnFot23VfgrGLS3vvLWMBFSrmyfrRf1t/fLvpLm0uChbba8hW0yy5pQFr96sJOwFzAB3hXSrlOCHE5MF4I8TDaRPUjrdXuC+EcYlECZAC0ty+pc/x8HAZoh19SrwEDgCQgWEr5HyFELFo5o0umv8BZx6I995ez/Wy0u75SPxI1D23ULgvoAXwkhJDA81xCfeUcYtGe+8q5fD7aVX8RQggppRRC3A9cCSQIIToCywAD2nxob9pBf2lz1SGENok6QEr5Y33C87qU8mYhxJNAzomrUOs/tC7AtWgdc5OU0twqDb8AVCwaUzFpTAjxKNqoRQbwGzAOeAwtHotOOK7dx0PFwpaKRwOhrWR/Ukr5pBDCAXgX7VL+RKBUSvnpCce263ioWDSmYqIRQjiilX0rFkI8BuRJKRcJIW5AWxgZBfwppfzuhMdctPFoE0mwEKIH8Dcp5dzjZyAn3D9JSvmGEML+hEvedlJK84nHthcqFo2pmNiqf9831n9ZuwD3AgFoixLGoCU7+VLKdfXHt9t4qFjYUvGw9Zd4eKG9/5lANtoGB79LKf97wvHtNh4qFo2pmNgSQtwK3IiWyH6FVg6vBK3ubyTaCUGVlPKr+uMv+ni0lYVxHYEbhBDd6ofgj0/TqACmC237PaOon5B9/AzjYg36aahYNKZiYqsjcJMQomf9JclFwHa0M/SX0OazTRBCeNR/ObXneKhY2FLxsHU8Hr2klKXAl8AzwKNo3x8loP1nDu3+u0PFojEVk3pCCH+0He/uARKArkA60BdtwV8GkA8ECSHc2sv3R6skwUKICCHELCFEcP1dLmiX7P4NIKU01Z9hHAW2AlPq72+TJTbOhYpFYyomtk4Rj6cApJT5aPOz1kkpU9HmbAVLKcsv5i+npqhY2FLxsNWMeLyLNh/6baAMLQmyJjftiYpFYyomtk6IRxBaabclUsoUYCMwQ0r5I9p3xlS06g9lwAgpZWV7+f5o8SS4/vLD90Av4CkhxCDgJynlw4BOCHHdCcc6oBVjdhBttNDyuVCxaEzFxNZp4iGEEDPrD80DHhZCfAe8inZy0K6oWNhS8bB1mnhwYjyklFXADiC1PX53qFg0pmJi64R49EYb+XaXUn5b/+NitIXnAJ+jbQSyApgOJLZwUy8sKWWL/EGbMP13tCH2RfX3TUIrczWm/vZAtN1XdCc8bjDg3FLtVLFQMWkLf84gHquOxwNt1OJvaAsHW/09qFioeLSxePz1u2NYe/vuULFQMTmHeDwKDKu/PRx4s/7fPQAn4BpgAuDf2u/hfP654Avj6udvflZ/swZtOF2glbmqQ7uU3RF4T0pZKYR4HyiSUj5zQRvWClQsGlMxsXWW8SiWUv67Ndp7IalY2FLxsKW+OxqoWDSmYmLrDOLxGtq84AFoyW8+WtWMypZuc0toiWF+C3BESjkHeAEYgTb3pKPULjkkAQ7U7zCCNu9zQwu0qzWoWDSmYmLrbOKxvhXa2RJULGypeNhS3x0NVCwaUzGx1Zx4uAChaKXQhgHfSCkfbK8JMLRMEqxDm2SNlDIbOIBWfmSa0IovJwFj0c5MkFIWy/ryPe2QikVjKia2VDwaqFjYUvGwpeLRQMWiMRUTW82Jx8j6Y7+QUnaSUv7cKi1tQRd8xzgppYn6wAtte8FoKeVlQojrgf+glSDJAtrtmcZxKhaNqZjYUvFooGJhS8XDlopHAxWLxlRMbDUzHjloW0VntF5LW1aLbZtcv8LSCGwVQnQFOgO/ogU+U0qZ11JtaW0qFo2pmNhS8WigYmFLxcOWikcDFYvGVExsNSMe+a3ZvpbWojvGCSGuQNt7eg3aXJNFp3lIu6Vi0ZiKiS0VjwYqFrZUPGypeDRQsWhMxcSWikeDlk6Cx6KVtHpTSlnXYi/cBqlYNKZiYkvFo4GKhS0VD1sqHg1ULBpTMbGl4tGgpZPgi3Z/6fNNxaIxFRNbKh4NVCxsqXjYUvFooGLRmIqJLRWPBi2aBCuKoiiKoihKW9AutwNUFEVRFEVRlFNRSbCiKIqiKIpyyVFJsKIoShshhKgSQmz+y5+jQoh7TjjmRSHERCGEvRBid/19ZUKIDUKI9PqV34qiKMpptFidYEVRFOW0jkopR5x4hxDiacBU/+/xwBxgGlAKxAoh7gSSpZRjhBDPAZf0am9FUZTmUiPBiqIobYf5VPdLKX8HPgYellKOARKllJ8AlpZpnqIoSvuhRoIVRVHajj88pskAAAE/SURBVBAhxIa/3BcJPP+X+94WQpSecLtD/eOigD8vWOsURVHaEZUEK4qitB2Z9SO8VvXTIf7qNyAZuKP+dj5wN3D/BW2doihKO6KSYEVRlLZDnPKHQjwA3AAUArFAtBDifqAS8ANcLngLFUVR2gmVBCuKorQdJ0uCdQBSyveEEEZgG+AO+AB2wDop5WYhxGUt00xFUZSLn0qCFUVR2o7Ik8wJfhlACDEOmAzMR/v+/hToCVwvhHAFwoEtLdZaRVGUi5hKghVFUdqOvJPMCT7+XR0P3CKltAB1QohngUlSymQhhBdQg1oYpyiK0ixCStnabVAURVEURVGUFqXqBCuKoiiKoiiXHJUEK4qiKIqiKJcclQQriqIoiqIolxyVBCuKoiiKoiiXHJUEK4qiKIqiKJcclQQriqIoiqIol5z/BzDZijQK093nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    " # 将股价按首个交易日进行归一处理并可视化\n",
    "(data/data.iloc[0]).plot(figsize = (12,6)) \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 按照对数收益率的计算公式得到股票收益率\n",
    "R = np.log(data/data.shift(1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 删除缺省的数据\n",
    "R = R.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>顺发恒业</th>\n",
       "      <th>重庆啤酒</th>\n",
       "      <th>中南传媒</th>\n",
       "      <th>贵州茅台</th>\n",
       "      <th>五粮液</th>\n",
       "      <th>海天味业</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>730.000000</td>\n",
       "      <td>730.000000</td>\n",
       "      <td>730.000000</td>\n",
       "      <td>730.000000</td>\n",
       "      <td>730.000000</td>\n",
       "      <td>730.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-0.000634</td>\n",
       "      <td>0.001430</td>\n",
       "      <td>-0.000465</td>\n",
       "      <td>0.001730</td>\n",
       "      <td>0.001842</td>\n",
       "      <td>0.001761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.018325</td>\n",
       "      <td>0.023926</td>\n",
       "      <td>0.016921</td>\n",
       "      <td>0.019666</td>\n",
       "      <td>0.023897</td>\n",
       "      <td>0.020767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-0.107948</td>\n",
       "      <td>-0.080702</td>\n",
       "      <td>-0.106236</td>\n",
       "      <td>-0.105361</td>\n",
       "      <td>-0.105404</td>\n",
       "      <td>-0.105361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-0.009185</td>\n",
       "      <td>-0.012824</td>\n",
       "      <td>-0.008962</td>\n",
       "      <td>-0.009260</td>\n",
       "      <td>-0.012373</td>\n",
       "      <td>-0.010169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000472</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000337</td>\n",
       "      <td>0.000171</td>\n",
       "      <td>0.000957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.007299</td>\n",
       "      <td>0.014430</td>\n",
       "      <td>0.008031</td>\n",
       "      <td>0.012650</td>\n",
       "      <td>0.014420</td>\n",
       "      <td>0.012534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.096309</td>\n",
       "      <td>0.084434</td>\n",
       "      <td>0.062113</td>\n",
       "      <td>0.078877</td>\n",
       "      <td>0.095333</td>\n",
       "      <td>0.095310</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             顺发恒业        重庆啤酒        中南传媒        贵州茅台         五粮液        海天味业\n",
       "count  730.000000  730.000000  730.000000  730.000000  730.000000  730.000000\n",
       "mean    -0.000634    0.001430   -0.000465    0.001730    0.001842    0.001761\n",
       "std      0.018325    0.023926    0.016921    0.019666    0.023897    0.020767\n",
       "min     -0.107948   -0.080702   -0.106236   -0.105361   -0.105404   -0.105361\n",
       "25%     -0.009185   -0.012824   -0.008962   -0.009260   -0.012373   -0.010169\n",
       "50%      0.000000    0.000472    0.000000    0.000337    0.000171    0.000957\n",
       "75%      0.007299    0.014430    0.008031    0.012650    0.014420    0.012534\n",
       "max      0.096309    0.084434    0.062113    0.078877    0.095333    0.095310"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "R.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 将股票收益率按照直方图范式展示\n",
    "R.hist(bins = 40,figsize = (10, 10))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 计算股票的年化平均收益率\n",
    "R_mean = R.mean()*252"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "顺发恒业   -0.159700\n",
       "重庆啤酒    0.360247\n",
       "中南传媒   -0.117060\n",
       "贵州茅台    0.435992\n",
       "五粮液     0.464245\n",
       "海天味业    0.443853\n",
       "dtype: float64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "R_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 计算股票的协方差矩阵并做年化处理\n",
    "R_cov = R.cov()*252"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>顺发恒业</th>\n",
       "      <th>重庆啤酒</th>\n",
       "      <th>中南传媒</th>\n",
       "      <th>贵州茅台</th>\n",
       "      <th>五粮液</th>\n",
       "      <th>海天味业</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>顺发恒业</th>\n",
       "      <td>0.084622</td>\n",
       "      <td>0.033247</td>\n",
       "      <td>0.021737</td>\n",
       "      <td>0.027406</td>\n",
       "      <td>0.035941</td>\n",
       "      <td>0.021226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>重庆啤酒</th>\n",
       "      <td>0.033247</td>\n",
       "      <td>0.144262</td>\n",
       "      <td>0.022357</td>\n",
       "      <td>0.047254</td>\n",
       "      <td>0.061413</td>\n",
       "      <td>0.047958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>中南传媒</th>\n",
       "      <td>0.021737</td>\n",
       "      <td>0.022357</td>\n",
       "      <td>0.072151</td>\n",
       "      <td>0.012028</td>\n",
       "      <td>0.014914</td>\n",
       "      <td>0.009723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>贵州茅台</th>\n",
       "      <td>0.027406</td>\n",
       "      <td>0.047254</td>\n",
       "      <td>0.012028</td>\n",
       "      <td>0.097465</td>\n",
       "      <td>0.095358</td>\n",
       "      <td>0.056486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>五粮液</th>\n",
       "      <td>0.035941</td>\n",
       "      <td>0.061413</td>\n",
       "      <td>0.014914</td>\n",
       "      <td>0.095358</td>\n",
       "      <td>0.143906</td>\n",
       "      <td>0.070205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>海天味业</th>\n",
       "      <td>0.021226</td>\n",
       "      <td>0.047958</td>\n",
       "      <td>0.009723</td>\n",
       "      <td>0.056486</td>\n",
       "      <td>0.070205</td>\n",
       "      <td>0.108675</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          顺发恒业      重庆啤酒      中南传媒      贵州茅台       五粮液      海天味业\n",
       "顺发恒业  0.084622  0.033247  0.021737  0.027406  0.035941  0.021226\n",
       "重庆啤酒  0.033247  0.144262  0.022357  0.047254  0.061413  0.047958\n",
       "中南传媒  0.021737  0.022357  0.072151  0.012028  0.014914  0.009723\n",
       "贵州茅台  0.027406  0.047254  0.012028  0.097465  0.095358  0.056486\n",
       "五粮液   0.035941  0.061413  0.014914  0.095358  0.143906  0.070205\n",
       "海天味业  0.021226  0.047958  0.009723  0.056486  0.070205  0.108675"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "R_cov"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 计算股票的相关系数矩阵\n",
    "R_corr = R.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>顺发恒业</th>\n",
       "      <th>重庆啤酒</th>\n",
       "      <th>中南传媒</th>\n",
       "      <th>贵州茅台</th>\n",
       "      <th>五粮液</th>\n",
       "      <th>海天味业</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>顺发恒业</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.300907</td>\n",
       "      <td>0.278185</td>\n",
       "      <td>0.301770</td>\n",
       "      <td>0.325695</td>\n",
       "      <td>0.221345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>重庆啤酒</th>\n",
       "      <td>0.300907</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.219136</td>\n",
       "      <td>0.398512</td>\n",
       "      <td>0.426231</td>\n",
       "      <td>0.383019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>中南传媒</th>\n",
       "      <td>0.278185</td>\n",
       "      <td>0.219136</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.143427</td>\n",
       "      <td>0.146361</td>\n",
       "      <td>0.109804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>贵州茅台</th>\n",
       "      <td>0.301770</td>\n",
       "      <td>0.398512</td>\n",
       "      <td>0.143427</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.805179</td>\n",
       "      <td>0.548851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>五粮液</th>\n",
       "      <td>0.325695</td>\n",
       "      <td>0.426231</td>\n",
       "      <td>0.146361</td>\n",
       "      <td>0.805179</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.561386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>海天味业</th>\n",
       "      <td>0.221345</td>\n",
       "      <td>0.383019</td>\n",
       "      <td>0.109804</td>\n",
       "      <td>0.548851</td>\n",
       "      <td>0.561386</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          顺发恒业      重庆啤酒      中南传媒      贵州茅台       五粮液      海天味业\n",
       "顺发恒业  1.000000  0.300907  0.278185  0.301770  0.325695  0.221345\n",
       "重庆啤酒  0.300907  1.000000  0.219136  0.398512  0.426231  0.383019\n",
       "中南传媒  0.278185  0.219136  1.000000  0.143427  0.146361  0.109804\n",
       "贵州茅台  0.301770  0.398512  0.143427  1.000000  0.805179  0.548851\n",
       "五粮液   0.325695  0.426231  0.146361  0.805179  1.000000  0.561386\n",
       "海天味业  0.221345  0.383019  0.109804  0.548851  0.561386  1.000000"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "R_corr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 计算股票收益率的年化波动率\n",
    "R_vol = R.std()*np.sqrt(252)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "顺发恒业    0.290898\n",
       "重庆啤酒    0.379818\n",
       "中南传媒    0.268609\n",
       "贵州茅台    0.312193\n",
       "五粮液     0.379350\n",
       "海天味业    0.329659\n",
       "dtype: float64"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "R_vol"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.00649757, 0.14317448, 0.25598717, 0.22760314, 0.02400595,\n",
       "       0.34273169])"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 计算投资组合的预期收益率\n",
    "R_port = np.sum(weights*R_mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "投资组合的预期收益率： 0.2831\n"
     ]
    }
   ],
   "source": [
    "print('投资组合的预期收益率：',round(R_port, 4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 计算投资组合收益波动率\n",
    "vol_port = np.sqrt(np.dot(weights, np.dot(R_cov, weights.T)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "投资组合收益波动率： 0.2237\n"
     ]
    }
   ],
   "source": [
    "print('投资组合收益波动率：', round(vol_port, 4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 为了后续方便，定义一个组合函数\n",
    "def port_R_vol(weights):\n",
    "    '''\n",
    "    weights：组合中各资产的比例\n",
    "    '''\n",
    "    R_mean = R.mean()*252\n",
    "    R_cov = R.cov()*252\n",
    "    R_port = np.sum(weights*R_mean)\n",
    "    vol_port = np.sqrt(np.dot(weights, np.dot(R_cov, weights.T)))\n",
    "    return R_port,vol_port"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 投资组合的有效前沿"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 绘制可行集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "Rp_list = []  # 建立一个初始的投资组合收益率数列\n",
    "Vp_list = []  # 建立一个初始的投资组合波动率数列"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in np.arange(1000):  # 构建1000个不同权重的预期收益率与波动率\n",
    "    weights = get_weights(nums)\n",
    "    Rp_list.append(port_R_vol(weights)[0])\n",
    "    Vp_list.append(port_R_vol(weights)[1]) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 可视化投资组合可行集\n",
    "plt.figure(figsize=(8,6))  # 创建一个画布\n",
    "plt.scatter(Vp_list, Rp_list)  # 散点图\n",
    "plt.xlabel('波动率', fontsize = 13)\n",
    "plt.ylabel('收益率', fontsize = 13, rotation = 0)\n",
    "plt.xticks(fontsize = 13)\n",
    "plt.yticks(fontsize = 13)\n",
    "plt.xlim(0.18, 0.34)\n",
    "plt.ylim(-0.1, 0.5)\n",
    "plt.title('投资组合收益率与波动率的关系', fontsize = 13)\n",
    "plt.grid('True')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 为了避免后面重复编码，封装函数 draw_set()\n",
    "def draw_set(Vp_list, Rp_list):\n",
    "    plt.figure(figsize=(8,6))  # 创建一个画布\n",
    "    plt.scatter(Vp_list, Rp_list)  # 散点图\n",
    "    plt.xlabel('波动率', fontsize = 13)\n",
    "    plt.ylabel('收益率', fontsize = 13, rotation = 0)\n",
    "    plt.xticks(fontsize = 13)\n",
    "    plt.yticks(fontsize = 13)\n",
    "    plt.xlim(0.18, 0.34)\n",
    "    plt.ylim(-0.1, 0.5)\n",
    "    plt.grid('True')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 构建有效前沿"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 计算波动率最小情况下的股票配置权重"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "import scipy.optimize as sco  # 导入 SciPy 的子模块 optimize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 定义最优化函数\n",
    "def f(w):\n",
    "    w = np.array(w)  # 设置投资组合中每只股票的权重\n",
    "    Rp_opt = port_R_vol(w)[0]  # 计算最优投资组合的预期收益率\n",
    "    Vp_opt = port_R_vol(w)[1]  # 计算最优投资组合的收益波动率\n",
    "    return np.array([Rp_opt, Vp_opt])  # 以数组格式输出结果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 定义得到最小波动率的权重函数\n",
    "def Vmin_f(w):\n",
    "    return f(w)[1]  # 输出函数 f(w) 的第二个函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 以字典格式依次输入权重的约束条件：预期收益率 = 10%\n",
    "R_exp = 0.1\n",
    "cons = ({'type':'eq','fun': lambda x:np.sum(x)-1},{'type':'eq','fun': lambda x:f(x)[0]-R_exp})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "({'type': 'eq', 'fun': <function __main__.<lambda>(x)>},\n",
       " {'type': 'eq', 'fun': <function __main__.<lambda>(x)>})"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cons"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 以元组格式输入权重的边界条件\n",
    "bnds = tuple((0,1) for x in range(len(R_mean)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((0, 1), (0, 1), (0, 1), (0, 1), (0, 1), (0, 1))"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bnds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "eq_weights = len(R_mean)*[1/len(R_mean),]  # 用于生成一个权重相等的数组"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.16666666666666666,\n",
       " 0.16666666666666666,\n",
       " 0.16666666666666666,\n",
       " 0.16666666666666666,\n",
       " 0.16666666666666666,\n",
       " 0.16666666666666666]"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eq_weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "result = sco.minimize(Vmin_f,eq_weights, method='SLSQP', bounds = bnds, constraints=cons)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "     fun: 0.19558462183864725\n",
       "     jac: array([0.18636462, 0.20441259, 0.18831571, 0.20698765, 0.23632165,\n",
       "       0.20752008])\n",
       " message: 'Optimization terminated successfully'\n",
       "    nfev: 56\n",
       "     nit: 8\n",
       "    njev: 8\n",
       "  status: 0\n",
       " success: True\n",
       "       x: array([0.21003989, 0.05075644, 0.3769439 , 0.17893455, 0.        ,\n",
       "       0.18332522])"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['顺发恒业', '贵州茅台', '重庆啤酒', '中南传媒', '海天味业', '五粮液']"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "投资组合预期收益率10%时顺发恒业的权重：21.00%\n",
      "投资组合预期收益率10%时贵州茅台的权重：5.08%\n",
      "投资组合预期收益率10%时重庆啤酒的权重：37.69%\n",
      "投资组合预期收益率10%时中南传媒的权重：17.89%\n",
      "投资组合预期收益率10%时海天味业的权重：0.00%\n",
      "投资组合预期收益率10%时五粮液的权重：18.33%\n"
     ]
    }
   ],
   "source": [
    "for i in range(len(names)):\n",
    "    print('投资组合预期收益率{:.0%}时{}的权重：{:.2%}'.format(R_exp,names[i],round(result['x'][i],4)) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 饼图可视化投资组合中各股票权重\n",
    "plt.pie(list(result['x']),labels=names, autopct = '%.1f%%',shadow = True)\n",
    "plt.title('投资组合各股票权重')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 计算波动率的全局最小值与预期收益"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 设置约束条件：权重和为1\n",
    "cons_vmin = ({'type': 'eq', 'fun': lambda x: np.sum(x)-1})  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 最优化求解：minimize 函数\n",
    "result_vmin = sco.minimize(Vmin_f,eq_weights, method = 'SLSQP', bounds=bnds, constraints=cons_vmin)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 计算全局方差最小投资组合的预期收益率\n",
    "Rp_vmin = np.sum(R_mean*result_vmin['x'])\n",
    "# 计算全局方差最小投资组合的波动率\n",
    "Vp_vmin = result_vmin['fun']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "全局最小波动率对应投资组合的预期收益率 0.0735\n",
      "全局最小波动率 0.1951\n"
     ]
    }
   ],
   "source": [
    "print('全局最小波动率对应投资组合的预期收益率',round(Rp_vmin, 4))\n",
    "print('全局最小波动率', round(Vp_vmin, 4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 绘制有效前沿"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 生成投资组合的目标收益率数组\n",
    "Rp_target = np.linspace(Rp_vmin, 0.45, 100)\n",
    "# 目标波动率\n",
    "Vp_target = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 生成不同收益率下的最小波动率\n",
    "for r in Rp_target:\n",
    "    cons_new = ({'type':'eq','fun': lambda x:np.sum(x)-1},{'type':'eq','fun': lambda x:f(x)[0]- r}) # 以字典格式依次输入权重的约束条件\n",
    "    result_new = sco.minimize(Vmin_f,eq_weights, method='SLSQP', bounds = bnds, constraints=cons_new)\n",
    "    Vp_target.append(result_new['fun'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制有效前沿\n",
    "def draw_frontier(Vp_list, Rp_list,Vp_vmin, Rp_vmin):\n",
    "    draw_set(Vp_list, Rp_list)  # 可行集\n",
    "    plt.plot(Vp_target, Rp_target, 'c-', label = '有效前沿', lw = 2.5)\n",
    "    plt.plot(Vp_vmin, Rp_vmin, 'r*', label = '全局最小方差组合', markersize = 14)\n",
    "    plt.legend(fontsize = 13)\n",
    "\n",
    "draw_frontier(Vp_list, Rp_list,Vp_vmin, Rp_vmin)\n",
    "plt.title('投资组合的有效前沿', fontsize = 13)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 资本配置线"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 计算：资本配置线的斜率、最优风险组合预期收益率、收益波动率"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 定义一个新的需要求解最优值的函数\n",
    "def F(w):\n",
    "    Rf = 0.02  # 无风险利率为2%\n",
    "    w = np.array(w)  # 设置投资组合中每只股票的权重\n",
    "    Rp_opt = port_R_vol(w)[0]  # 计算最优组合的预期收益率\n",
    "    Vp_opt = port_R_vol(w)[1]  # 计算最优投资组合收益波动率\n",
    "    SR = (Rp_opt - Rf)/Vp_opt\n",
    "    return np.array([Rp_opt, Vp_opt, SR])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 定义一个使得负的夏普比率最小化的函数 → 夏普比率最大\n",
    "def SRmin_F(w):\n",
    "    return -F(w)[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 以字典格式依次输入权重的约束条件\n",
    "cons_SR = ({'type':'eq','fun': lambda x:np.sum(x)-1}) \n",
    "# 最优化求解：minimize\n",
    "result_SR = sco.minimize(SRmin_F,eq_weights, method='SLSQP', bounds = bnds, constraints=cons_SR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "最优风险组合的预期收益率： 0.4287\n",
      "最优风险组合的波动率： 0.2704\n"
     ]
    }
   ],
   "source": [
    "Rf = 0.02\n",
    "slope = -result_SR['fun']\n",
    "Rm = np.sum(R_mean*result_SR['x'])\n",
    "Vm = (Rm-Rf)/slope\n",
    "print('最优风险组合的预期收益率：', round(Rm, 4))\n",
    "print('最优风险组合的波动率：', round(Vm, 4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "####  资本配置线可视化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 刻画资本配置线的投资组合预期收益率数组\n",
    "Rp_cml = np.linspace(0.18, 0.6)  \n",
    "# 刻画资本配置线的投资组合收益波动率数组\n",
    "Vp_cml = (Rp_cml -  Rf)/slope  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制资本配置线\n",
    "draw_frontier(Vp_list, Rp_list,Vp_vmin, Rp_vmin)\n",
    "\n",
    "plt.plot(Vp_cml, Rp_cml, 'b--', label = '资本配置线', lw = 2.5)\n",
    "plt.plot(Vm, Rm, 'ro', label = '全局最小波动率', markersize = 14)\n",
    "\n",
    "plt.title('资本配置线', fontsize = 13)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 资本资产定价模型"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 系统性风险 vs. 非系统性风险"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 导入数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 导入外部数据: 'data/上证180指数成分股日收盘价（2017-2019年（剔除该期间上市和存在退市风险的股票））.xlsx'\n",
    "stocks_data = pd.read_excel('data/上证180指数成分股日收盘价（2017-2019年（剔除该期间上市和存在退市风险的股票））.xlsx',\\\n",
    "                            sheet_name = 'Sheet1',header = 0, index_col = 0, parse_dates=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['航天信息', '新湖中宝', '生物股份', '复星医药', '兖州煤业', '生益科技', '雅戈尔', '中国巨石', '华创阳安',\n",
       "       '中国船舶',\n",
       "       ...\n",
       "       '国电南瑞', '海澜之家', '金地集团', '江西铜业', '浙江龙盛', '恒力石化', '华夏幸福', '白云山', '万华化学',\n",
       "       '安琪酵母'],\n",
       "      dtype='object', length=135)"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stocks_data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2017-01-03', '2017-01-04', '2017-01-05', '2017-01-06',\n",
       "               '2017-01-09', '2017-01-10', '2017-01-11', '2017-01-12',\n",
       "               '2017-01-13', '2017-01-16',\n",
       "               ...\n",
       "               '2019-12-18', '2019-12-19', '2019-12-20', '2019-12-23',\n",
       "               '2019-12-24', '2019-12-25', '2019-12-26', '2019-12-27',\n",
       "               '2019-12-30', '2019-12-31'],\n",
       "              dtype='datetime64[ns]', name='交易日期', length=731, freq=None)"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stocks_data.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>航天信息</th>\n",
       "      <th>新湖中宝</th>\n",
       "      <th>生物股份</th>\n",
       "      <th>复星医药</th>\n",
       "      <th>兖州煤业</th>\n",
       "      <th>生益科技</th>\n",
       "      <th>雅戈尔</th>\n",
       "      <th>中国巨石</th>\n",
       "      <th>华创阳安</th>\n",
       "      <th>中国船舶</th>\n",
       "      <th>...</th>\n",
       "      <th>国电南瑞</th>\n",
       "      <th>海澜之家</th>\n",
       "      <th>金地集团</th>\n",
       "      <th>江西铜业</th>\n",
       "      <th>浙江龙盛</th>\n",
       "      <th>恒力石化</th>\n",
       "      <th>华夏幸福</th>\n",
       "      <th>白云山</th>\n",
       "      <th>万华化学</th>\n",
       "      <th>安琪酵母</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>交易日期</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-01-03</th>\n",
       "      <td>23.67</td>\n",
       "      <td>10.88</td>\n",
       "      <td>10.97</td>\n",
       "      <td>14.03</td>\n",
       "      <td>15.11</td>\n",
       "      <td>31.91</td>\n",
       "      <td>7.14</td>\n",
       "      <td>12.43</td>\n",
       "      <td>13.32</td>\n",
       "      <td>23.89</td>\n",
       "      <td>...</td>\n",
       "      <td>11.82</td>\n",
       "      <td>7.16</td>\n",
       "      <td>25.99</td>\n",
       "      <td>4.80</td>\n",
       "      <td>11.39</td>\n",
       "      <td>10.55</td>\n",
       "      <td>10.28</td>\n",
       "      <td>18.70</td>\n",
       "      <td>25.53</td>\n",
       "      <td>28.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-04</th>\n",
       "      <td>23.78</td>\n",
       "      <td>10.90</td>\n",
       "      <td>12.07</td>\n",
       "      <td>14.07</td>\n",
       "      <td>15.11</td>\n",
       "      <td>31.95</td>\n",
       "      <td>7.20</td>\n",
       "      <td>12.54</td>\n",
       "      <td>13.39</td>\n",
       "      <td>24.29</td>\n",
       "      <td>...</td>\n",
       "      <td>11.91</td>\n",
       "      <td>7.15</td>\n",
       "      <td>26.08</td>\n",
       "      <td>4.81</td>\n",
       "      <td>11.23</td>\n",
       "      <td>10.55</td>\n",
       "      <td>10.41</td>\n",
       "      <td>18.86</td>\n",
       "      <td>25.85</td>\n",
       "      <td>30.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-05</th>\n",
       "      <td>23.61</td>\n",
       "      <td>11.04</td>\n",
       "      <td>11.95</td>\n",
       "      <td>14.00</td>\n",
       "      <td>15.14</td>\n",
       "      <td>32.24</td>\n",
       "      <td>7.35</td>\n",
       "      <td>12.51</td>\n",
       "      <td>13.37</td>\n",
       "      <td>24.05</td>\n",
       "      <td>...</td>\n",
       "      <td>11.87</td>\n",
       "      <td>7.10</td>\n",
       "      <td>27.05</td>\n",
       "      <td>4.79</td>\n",
       "      <td>11.29</td>\n",
       "      <td>10.58</td>\n",
       "      <td>10.32</td>\n",
       "      <td>19.00</td>\n",
       "      <td>25.59</td>\n",
       "      <td>29.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-06</th>\n",
       "      <td>23.35</td>\n",
       "      <td>10.73</td>\n",
       "      <td>11.74</td>\n",
       "      <td>13.87</td>\n",
       "      <td>14.72</td>\n",
       "      <td>32.18</td>\n",
       "      <td>7.23</td>\n",
       "      <td>12.44</td>\n",
       "      <td>13.18</td>\n",
       "      <td>23.91</td>\n",
       "      <td>...</td>\n",
       "      <td>11.89</td>\n",
       "      <td>7.03</td>\n",
       "      <td>26.55</td>\n",
       "      <td>4.88</td>\n",
       "      <td>11.27</td>\n",
       "      <td>10.30</td>\n",
       "      <td>10.18</td>\n",
       "      <td>18.95</td>\n",
       "      <td>25.17</td>\n",
       "      <td>29.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-09</th>\n",
       "      <td>23.41</td>\n",
       "      <td>10.80</td>\n",
       "      <td>11.74</td>\n",
       "      <td>13.89</td>\n",
       "      <td>14.36</td>\n",
       "      <td>33.48</td>\n",
       "      <td>7.42</td>\n",
       "      <td>12.46</td>\n",
       "      <td>13.20</td>\n",
       "      <td>24.16</td>\n",
       "      <td>...</td>\n",
       "      <td>11.92</td>\n",
       "      <td>7.07</td>\n",
       "      <td>26.72</td>\n",
       "      <td>4.95</td>\n",
       "      <td>11.50</td>\n",
       "      <td>10.23</td>\n",
       "      <td>10.30</td>\n",
       "      <td>18.99</td>\n",
       "      <td>25.26</td>\n",
       "      <td>30.42</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 135 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             航天信息   新湖中宝   生物股份   复星医药   兖州煤业   生益科技   雅戈尔   中国巨石   华创阳安  \\\n",
       "交易日期                                                                       \n",
       "2017-01-03  23.67  10.88  10.97  14.03  15.11  31.91  7.14  12.43  13.32   \n",
       "2017-01-04  23.78  10.90  12.07  14.07  15.11  31.95  7.20  12.54  13.39   \n",
       "2017-01-05  23.61  11.04  11.95  14.00  15.14  32.24  7.35  12.51  13.37   \n",
       "2017-01-06  23.35  10.73  11.74  13.87  14.72  32.18  7.23  12.44  13.18   \n",
       "2017-01-09  23.41  10.80  11.74  13.89  14.36  33.48  7.42  12.46  13.20   \n",
       "\n",
       "             中国船舶  ...   国电南瑞  海澜之家   金地集团  江西铜业   浙江龙盛   恒力石化   华夏幸福    白云山  \\\n",
       "交易日期               ...                                                         \n",
       "2017-01-03  23.89  ...  11.82  7.16  25.99  4.80  11.39  10.55  10.28  18.70   \n",
       "2017-01-04  24.29  ...  11.91  7.15  26.08  4.81  11.23  10.55  10.41  18.86   \n",
       "2017-01-05  24.05  ...  11.87  7.10  27.05  4.79  11.29  10.58  10.32  19.00   \n",
       "2017-01-06  23.91  ...  11.89  7.03  26.55  4.88  11.27  10.30  10.18  18.95   \n",
       "2017-01-09  24.16  ...  11.92  7.07  26.72  4.95  11.50  10.23  10.30  18.99   \n",
       "\n",
       "             万华化学   安琪酵母  \n",
       "交易日期                      \n",
       "2017-01-03  25.53  28.97  \n",
       "2017-01-04  25.85  30.09  \n",
       "2017-01-05  25.59  29.80  \n",
       "2017-01-06  25.17  29.85  \n",
       "2017-01-09  25.26  30.42  \n",
       "\n",
       "[5 rows x 135 columns]"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stocks_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 计算每只股票的日收益率"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 建立股票日收益率时间序列\n",
    "return_stocks = np.log(stocks_data/stocks_data.shift(1))  \n",
    "# 缺失数据的处理\n",
    "return_stocks = return_stocks.dropna()\n",
    "# 计算全部股票的数量\n",
    "n = len(return_stocks.columns)  \n",
    "# 生成存放投资组合收益波动率的初始数组\n",
    "vol_port = np.zeros(n)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 循环计算所有股票的日收益率\n",
    "for i in range(1, n+1):\n",
    "    # 依次生成投资组合中每只股票的等权重数组\n",
    "    weight = np.ones(i)/i\n",
    "    # 依次计算投资组合中股票收益率的年化协方差\n",
    "    return_cov = 252*return_stocks.iloc[:,:i].cov()  \n",
    "    # 依次计算并存放投资组合的年化波动率\n",
    "    vol_port[i-1] = np.sqrt(np.dot(weight, np.dot(return_cov, weight.T)))  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 可视化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 生成股票数量数组：从1到135\n",
    "N_list = np.arange(n)+1  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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tQFSZjyGSqfOJGp63iJqWVYlE8OfEb5yK72sAj4Z+yzT2S5KKN9294c2OUp+3J9ofLCIuWf0T8LaZ3UfcR+BL4ki9A1GD8qZHw7v2RHuKaUn5Ms27F8s2/mvpckvNp4pIAs4ADnb3t8xsF6KR4O4WN8Z6xN0XJuOfRNREbZuWCNTEIvIZFvevuJc4nbRH2ikWaU35bpSgrrQ64kjjC2InsTfwBNF4Z1/iqOb3xPnqqUSL5y2Iy6L+TpzXOyqZTzVx5HM/0XL69GT4QKIV/TbEzrUv0fhpDstelzyTaEn/q7TYTgP+0Ejck0muDW+ibGsTO9EOjXy+C3He1ogj9vnJd45Ohg8CehHncZcSRzMdiIZeeyXzWJU4PTG5wbx3J64WMOIo7H+JncbPiI1uJ2Jj+ThxNFpNHP2NT5ZTF6IG4FHqr/F+OFlO6f2PEtWrzf3OOxHn9p8BdlqRdSCZpmjXAyLR2CZ53xd4PO2znxPn8qeybEPFVEPENYkk5V3iKL9LMrwLcRrhCRpc4pg2jw2T+aa++y4iWWu0kSdxyuabZn67Q4FuDX7La6i/HG8WUfvwDdArGedtYPcm5nkxcS+An67gcjsl+d0eBdZq8B3difV+cbI8a4n/z0Ukl04S/5+XiP/ed9Om7Qj8sJDbxnLvUteCigBgZvsAX3k0FFuDOH98ly9bxV0DbEc0+jmMOK/6JPBvT6s6TRt/Y6Cnu49uizKsCDPr4u5zzGxNYoM0HTgauMWTI5lkvKFE6+rlLl80s92AZ919dhPfM5A4Il+SNmx9d3+vwXi93P3L5WaQRy1dB9x9cdK+ouTWg+R0TQ/gfW9iY9jwfH2W864CRrj7uKR/IPC1x9F5UTGznsC89HW8mfEzLjeLSxyXuvszTUy7OpFAvdrI53sQVyjoUsI2pGRARESkwulBRSIiIhVOyYCIiEiFq4irCXr37u0DBgxYoXnMnTuXzp1b7RHzJaHSylxp5YXKK3OllRdU5kqQqbwvvfTSDM/iZmgpFZEMDBgwgPHjx6/QPMaOHcvIkSNbJ6ASUWllrrTyQuWVudLKCypzJchUXjP7KPPYmek0gYiISIVTMiAiIlLhlAyIiIhUOCUDIiIiFU7JgIiISIVTMiAiIlLhlAyIiIhUOCUDIiIiFU7JgIiISIVTMiAiIlLhlAyIiIhUOCUDLTR2LPz737BgQaEjERERaR0V8aCi1nTYYTB1KkyZAv36FToaERGRFaeagRZq3z5eFy4sbBwiIiKtRclACykZEBGRcqNkoIVSycCiRYWNQ0REpLUoGWgh1QyIiEi5UTLQQkoGRESk3CgZaCElAyIiUm6UDLSQkgERESk3SgZaSMmAiIiUGyUDLaRkQEREyo2SgRZSMiAiIuVGyUALKRkQEZFyo2SghZQMiIhIuVEy0EJKBkREpNwoGWghJQMiIlJulAy0kJIBEREpN0oGWqhdu3hVMiAiIuVCyUALqWZARETKjZKBFlIyICIi5UbJQAspGRARkXKjZKCFlAyIiEi5UTLQQkoGRESk3CgZaCElAyIiUm6UDLRQKhlYtKiwcYiIiLQWJQMtpJoBEREpN0oGWkjJgIiIlBslAy2kZEBERMqNkoEWUjIgIiLlRslACykZEBGRcqNkoIWUDIiISLlRMtBCSgZERKTcKBloISUDIiJSbpQMtJCSARERKTdKBlqotjZelyyBurrCxiIiItIalAy0kJlqB0REpLwoGciBkgERESknSgZy0K5dvCoZEBGRcqBkIAeqGRARkXKiZCAHSgZERKScKBnIgZIBEREpJ0oGcqBkQEREyomSgRwoGRARkXKiZCAHSgZERKScKBnIQSoZWLSosHGIiIi0BiUDOVDNgIiIlBMlAzlQMiAiIuVEyUAOlAyIiEg5UTKQAyUDIiJSTpQM5EDJgIiIlBMlAzlQMiAiIuWkTZIBMxtpZhPM7DMzOzOL8Tcys7vM7HEzO8nMLBl+qJnNMbNpSfdw/qNfnpIBEREpJ3lPBsysJ3AvcDrQH9jezHZrYvxeyfg3ACcCxwKHJB+PAE5x91WTrtH55JOSARERKSdtUTOwBzDJ3e9x98XAlcChTYy/OnCmuz/g7m8CjwBDks82BcblNdosKBkQEZFy0hbJQH/gpbT+ScDAxkZ29zfcfRSAmW0C7AvcbWbVwHDgn2Y208yeNbMhjc0nn5QMiIhIOalpg++oAmal9c8FejQ3kZkdAfwVeAx4A+gF3AqcDXxGnHa4Fti6kemPB44H6NOnD2PHjs25AABz5sz5dh5TpvQF1mPSpE8ZO3biCs23mKWXuRJUWnmh8spcaeUFlbkStEp53T2vHfAT4Nq0/g2Bt7OctiswFjg1w2edAQe6NjefESNG+IoaM2bMt++vucYd3I89doVnW9TSy1wJKq287pVX5korr7vKXAkylRcY7y3YV7fFaYJngG3T+jcHPmpsZDMbYmYbA7j7N8B9wIZmNtjMdk4bdQ2gLunaVLt28arTBCIiUg7yngy4+xvATDO7yMx2BM4AbmlikjWBe8ysj5l1AL5PJBTtgevMbB0zWwn4A/Cgu8/NcxGWozYDIiJSTtrqpkP7AasBFwNXu/soMzvVzC5vOKK7PwRcA7wCTASeJU4zvJpM/zwwAZgPHN1G8S9DyYCIiJSTtmhAiLtPA45oMOwvTYx/AXBBhuFXEpcmFpSSARERKSe6HXEOlAyIiEg5UTKQAyUDIiJSTpQM5EDJgIiIlBMlAzlIJQOLFhU2DhERkdagZCAHqhkQEZFyomQgB0oGRESknCgZyIGSARERKSdKBnKgZEBERMqJkoEcKBkQEZFyomQgB0oGRESknCgZyEFNDZjB0qXRiYiIlDIlAzkwU+2AiIiUDyUDOVIyICIi5ULJQI6UDIiISLlQMpAjJQMiIlIulAzkSMmAiIiUCyUDOWrXLl6VDIiISKlTMpAj1QyIiEi5UDKQIyUDIiJSLpQM5EjJgIiIlAslAzlSMiAiIuVCyUCOlAyIiEi5UDKQIyUDIiJSLpQM5CiVDCxaVNg4REREVpSSgRypZkBERMqFkoEcKRkQEZFyoWQgR0oGRESkXCgZyJGSARERKRdKBnKkZEBERMqFkoEcKRkQEZFyoWQgR0oGRESkXCgZyJGSARERKRdKBnKkZEBERMqFkoEcKRkQEZFyoWQgR0oGRESkXCgZyJGSARERKRdKBnLUrl28KhkQEZFSp2QgR6oZEBGRcqFkIEdKBkREpFwoGciRkgERESkXSgZypGRARETKhZKBHCkZEBGRcqFkIEdKBkREpFwoGchRKhlYtKiwcYiIiKwoJQM5Us2AiIiUCyUDOVIyICIi5WKFkgEzqzKz3VsrmFKiZEBERMpFs8mAmXUwsxvMbB0z65sMW83M/gxUA7/Pd5DFqKYGqqqgrg6WLCl0NCIiIrnLpmZgKbABsA/wf2ZWDdwEfOjui5PPK5JqB0REpBw0mwwkO/y57n4p8DawEfAI8FZqlPyFV9yUDIiISDnIus2AmbUD7gPeBAYDt5pZb8DyFFvRUzIgIiLloKa5EcysU/J2A2AUsB/wHtDZ3WeYmWoGlAyIiEgJa7JmwMzWAz5PelcBfgI8BDwAbG9mXwAjzGy6mX2d10iL0CqrxOvbbxc2DhERkRXR3GmCScBmQB/gcmBL4Engr8AHRG3BG8AwYGj+wixOe+8dr3ffXdg4REREVkSTyYC7L3X3CUTtwMbApkBn4ASiIeFMYLG7f+7un+Q72GJzwAHxet99sHhxYWMRERHJVbYNCM3d5wI3AwcCuwODkysNKrbNwJAh0c2cCWPGFDoaERGR3GRz06EaoLOZ7QWcQtxXoBPws9Qo+Quv+KVqB+66q7BxiIiI5CqbmoFq4AXgfeBgd58HnA9sldyRsH0e4yt6Bx4Yr/feqzsRiohIacrmpkML3f2n7v6Ou7+XDFvs7lsDnwHn5DnGojZ0KAwcCDNmwBNPFDoaERGRlmvJTYdWM7MBZraSmbUHcPc6d78/i2lHmtkEM/vMzM7MYvyNzOwuM3vczE4yM0uGdzOze81shpk9ZmarZBt/vpjVnyq4887CxiIiIpKLljy18CzgCWA88ImZzTGzr8zsEzN7y8xOyzSRmfUE7gVOB/oT9yfYrbEvMbNeyfg3ACcCxwKHJB9fCdQBqwG3AVe0IP68SZ0quOceWFqxT2oQEZFS1dJHGP/C3dd295XdvQvQm7jHwEHAKY0cqe8BTHL3e5KrD64EDm3iO1YHznT3B9z9TeI5CEPMrBY4ADgnmc/1wHZm1rGFZWh1G24Ia64J06fDhAmFjkZERKRlmrwdsZltCOwAfAMMBBYkVfZfAB+4+xfALGCWmY0GuiefpesPvJTWPymZV0bu/gZxIyPMbBNgX2B/IvGoJnlAkrvXmdk0YABQ0F2wGfTrBx99FJcZioiIlJLmnk3Qm9hxLwFWBgYlw/oAQ81sKnAn8L/ufmAj86giEoaUuUCP5gIzsyOIOx0+RiQHqwKz3b2upfNqCz2SKGbNano8ERGRYtNkMuDuo4HRAGb2N+Ahd/9X6nMzW4O438DLZnakuz+bYTZfAmum9Xci7lXQJHe/2czuA/4N/BK4CuhqZubuqRsdNTovMzseOB6gT58+jB07trmvbNKcOXOanMfChYOAVXnuuQl06/Z5o+OVkubKXG4qrbxQeWWutPKCylwJWqW87p5VB/wG2Bn4XtqwvsDVxM76E2BEhuk2AN5J6z+OSCoa+54hwMZp/ScBNyXvXwK2SN53BeYDqzYX+4gRI3xFjRkzpsnPTzjBHdwvv3yFv6poNFfmclNp5XWvvDJXWnndVeZKkKm8wHjPcv/u7lk9wrgd0T6gn5lVA68C/zKzC4EfAouIhxn9F3gzQ7LxhpnNNLOLiMaAZxBXFjRmTeBqM9sC+Br4PvDP5LNrk89OJI76n3H3ac2VoS2kThN89VVh4xAREWmpbG46tIikKt7dlwKpR/K8DAym/kFFbyTZSCb7EZcDXgxc7e6jzOxUM7s8w/c9BFwDvAJMBJ4lkgDc/RpgFPEExXbAEdkWNN969oxXtRkQEZFS02zNQGJR2vv1zOw84O/uPsvMPjezNbyJpxYmR+9HNBj2lybGvwC4oJHPLgEuyTLuNqMGhCIiUqqyTQbapb3/AlgIPGxm44inFg4l2gxULCUDIiJSqrJ5amEVcUqA5B4Dc939D+4+GHiQuNxwaF6jLAFKBkREpFQ1WzPgcV3/fklvFXEHwdRnt5nZGHcvj2vpVoCSARERKVUtuh2xuy919+saDPvczLqbWe/WDa20KBkQEZFSlVUyYGY3mdl5ZtbLzPpkGGUH4k6EFUuXFoqISKnKtmbgu8Td/joAz5nZa2b28+QeBBBPFbwrHwGWiu7d43X2bKira3pcERGRYpJtMuDufoq7fwoY8HNgJPCOmf2YSBauz0+IpaG6Grp1A/dICEREREpFc08tfIa4x0Df5KmEBvRx96eBp83sz8TtiE9193l5j7bI9egRicCsWfWnDURERIpdczUDJxC1ANOBnybdTDPbx8weAHYCfgH8xMw65TXSEqBGhCIiUoqae2rhKwBmtsjd303eLyaeF/BX4BF3dzNbFTgROD/P8RY1JQMiIlKKsm0zYGY22szGAKsCtcA2wHbJjYguBo5M3lcsJQMiIlKKsr0d8Q+IhxU5UA10A9YDzgFWB34LnNHEg4oqgpIBEREpRdk8wnhz4A/uvrOZrUw8p6AO2A04FOgP3ATsmM9AS4HuNSAiIqWoydMESaPAUcADyaDzgVOBt4lHDL9E1BBs5O4f5zHOkqCaARERKUXN1Qx0Bu5y98vNbBCwOzDY3RcC55nZv4k7Dx5gZvtW+mmCnj3jVcmAiIiUkuauJpgO/C55/46ZbeHus9M+f9XMtgSGVXoiAKoZEBGR0tTSBxV93PCBRO4+3d3HtG5YpUnJgIiIlKIWJQNmNgR4w8w65CmekqZkQERESlG2lxamnES0IVhgZmsRyUTq9EBt6sZElUrJgIiIlKKsk4GkbcAewGlmVgXcTzQwNGA1YCqwdj6CLBW6tFBEREpRVqcJzGxN4AbgYODc1HTuvra7rwVMdPeKTgRANQMiInjRAncAACAASURBVFKamk0GknYC/wF+7O7PAIvcfUmD0Sr+SgKIRxibwTffwJKGS0hERKRINXfTod8CY4BDdMVA86qqoHv3eD97dtPjioiIFIvmagb+CTwLXGJm3dsgnpKnUwUiIlJqmkwG3H2Ku38feBh4IrnHQLtKfzphU5QMiIhIqcnqagJ3v8TMZgMPAvcSTy7EzM5NRuljZue6+1n5CbN0KBkQEZFSk/Wlhe7+dzMbCqzk7kvM7EqgO/EEwz8DtXmKsaQoGRARkVLT0psOnQG8Y2abufu1+Qio1OleAyIiUmpalAy4+1wz29Xd385XQKVONQMiIlJqWvRsAoDGEgEzO2XFwyl9eoyxiIiUmqxqBsxsK2BxU6MApwAXt0ZQpUw1AyIiUmqyPU3wCPAisdPfBBgPbJYMS2nXuqGVJiUDIiJSarJNBj509x0AzOwNd9/ezCa6+/apEcxsYl4iLDFKBkREpNRk22bAG3nf2DgVq2EycMcdcPTRsGhR4WISERFpSosbEKbRzj+D9GRg5kz40Y/ghhtg9OiChiUiItKobJOBYWY2z8zmA0PNbB6wTjIsNXzV/IVZOtLvM3DFFTBnTvRP1EkUEREpUtm2Geji7vOaGsHMvmiFeEpeKhmYMQMuv7x++HvvFSYeERGR5mRVM+Du88xsLzM7LzXMzJ5IXjcys9uBjfIUY0np2jUeZbxwYZwq6NIlhqtmQEREilWzyYCZjTCzdsC7QCczu9/MegFdzawzMBHYHVDNAGBWXzsAcP758aqaARERKVbZ1Az8BpgADAdOJR5OdBXQBdjU3ecSicLm+Qqy1KSSgc03h+OOi5qCjz6K2gIREZFi02wy4O4HAfsB2wAvAP2A44D2wAbJaK8A++cpxpLTr1+8nnEGtG8PAwZAXR1MmlTQsERERDLKqgGhu78GnGBmQ4AN3f0bMxvm7t8ko1wLzM5XkKXmuuuijcAee0T/+utHIvDeezB4cGFjExERaailTy1828wWmdnq7j41bfj41g+tdK23XnTp/Q8/rEaEIiJSnLJpQHiSmQ1MG3QYcH+G8X5nZiNbMbaysf768apGhCIiUoyyaUD4I+ApM9sm6d8duMvMnjGzMWb2tJltSjQ0/CpfgZayVC2BagZERKQYZXuaYA0AMzsW6E1cTfA/wPeTz7cCnkjaFkgDqhkQEZFilm0y8D1ih78psEvSgHChu78AYGaXAec1NYNK1r8/tGsHU6fG7YlTNyISEREpBk2eJkg7NbA6sAWRPKyc9nnn5O31wEP5CLAcVFfDOuvE+/ffL2wsIiIiDTXXZuA0YAhwu7tvkfTfb2ZrJZ+fYmaTgCXurqcYNkGnCkREpFg1mQy4++5EA8LnzWx9d38cuA74NfEI44uAHwLHm9kR+Q62lKWSATUiFBGRYpPN1QQ3AocAH5pZD2AUUAfg7gvc/WlgH+Cc5BkGkkHqigLVDIiISLHJJhm4AhhMNB58Bahx958DPc3sLDM7iziVMBrYO2+RljjVDIiISLFqrgHh5sBuwB3u/ixxyuAhMzsUuBxYBCxN5vMksG1+wy1dqhkQEZFi1dylhQuAo5InE+Luo81sf+AsYD93X5QaMUkcsqlpqEirrQadO8OXX8LMmbDSSoWOSEREJDTXgPA1d3+mwbAX3X2v9EQg0ZNoTyAZmOmKAhERKU5ZH8mbWbWZPZe875BhlL+j0wRNGjYsXl9+ubBxiIiIpMvmQUWDzGxtYBDQLRn8lZn908yGJuP0A9oDT+Qt0jKw6abxOm5cYeMQERFJl83tiMcDqWPZO5LXacB/gDvM7G6gK3CVu9e1fojlQ8mAiIgUo2ySgQ/dvWH1/2J3v8XM7iUuKRxM3LJYmrDRRlBTAxMm6BkFIiJSPLJpM9DDzPYzs43ShpmZbQX8DegETAF2yEeA5aRDB9hgA6irU7sBEREpHtkkA+OBY4D7zOx1MxsO9ANOIB5OtBFwBHCFmWWsaTCzkWY2wcw+M7Mzm/tCM/u9mc0ys3lmdo2ZVSfDDzWzOWY2LekezrKcRUOnCkREpNg0mwy4+/eTSwkHAP8E1gWucPcfuPut7l7n7q8CHwEHNJzezHoC9wKnA/2B7c1st8a+z8wOIO5kuCkwENgZODj5eARwiruvmnSNzqdYKRkQEZFi05JLC68Dnnf3O4DhZtarwSh/AZ7KMOkewCR3v8fdFwNXAoc28VWfAz9w94nu/jHwDDAg+WxToKR3o0oGRESk2DTZgNDMfggsJh5MtD5xVL8KcRnhWWaWfkOiWe7+aYbZ9AdeSuufRBzxZ+Tu3yYUyf0MdgSuTU4VDAf+mcTwDnCsu7/dVBmKzdCh0LEjTJoUdyPs1TClEhERaWPN1QxsR9xIaFtgZWAYsA3wAfEkw62T/m2Bm5JGhZm+Y1Za/1ygR5bxnQa8lSQIvYBbiYaKvYAHgWuznE/RqKmBjTeO9+PHFzYWERERAHP37EY0uwm4IXk+gQFTgW3c/f3k8+uBu939gQbT/QQY7u7HJ/0bAre5+5Bmvm9b4C5gE3efkuHzzsAcoJu7f5Ph8+OB4wH69Okz4rbbbsuqnI2ZM2cOXVrpWsC//nVd7r57DY455kOOOOKjVplnPrRmmUtBpZUXKq/MlVZeUJkrQabybr/99i+5+yZZz8Tds+qAjYHeaf2HAatnMd0GwDtp/ccBDzUzzQDgM2D3tGGDgZ3T+gcST0zs3FwMI0aM8BU1ZsyYFZ5HyqhR7uC+zz6tNsu8aM0yl4JKK6975ZW50srrrjJXgkzlBcZ7lvt3d8++AaG7v+LuM9L6b3H3qVlM9wYw08wuMrMdgTOAWxob38y6A48Bl7j7Q2kftQeuM7N1zGwl4A/Ag548UbGUqBGhiIgUk7Z65PB+wGrAxcDV7j7KzE41s8szjHskcfniKWn3E7jc4/LFi4HngQnAfODoNoq/Va27LnTvDp99Bp9manIpIiLShrK5HfEKc/dpxI2J0of9pZFxrwCuaOSzK4lLE0taVRVsthk89hjcfz/89KeFjkhERCpZW9UMSAPHHhuvF14IixcXNhYREalsSgYKZP/9YfBg+OgjuPnmQkcjIiKVTMlAgVRXw+mnx/s//hGWLClsPCIiUrmUDBTQwQdHY8JJk+DWWwsdjYiIVColAwVUU1NfO/D738Mpp8Auu8C228K0aYWNTUREKoeSgQI77DAYMAA++AAuuSSuMHjqKbj33kJHJiIilaJNLi2UxtXWRgPCf/wD1lkH3n03+t95p9CRiYhIpVAyUAS23jo6iPsOKBkQEZG2pNMERWbw4HidMKGwcYiISOVQMlBk1lorTh18/DHMmVPoaEREpBIoGSgyNTWw3nrx/r33ChuLiIhUBiUDRWjQoHhVuwEREWkLSgaKUKrdgJIBERFpC0oGilCqZkCNCEVEpC0oGShCOk0gIiJtSclAERo4MF7few+WLi1sLCIiUv6UDBShrl1hjTVg0SKYPLnQ0YiISLlTMlCk1G5ARETaipKBIqV2AyIi0laUDBQpJQMiItJWlAwUKd1rQERE2oqSgSKlmgEREWkrSgaK1GqrxVUFX34J06cXOhoRESlnSgaKlJlqB0REpG0oGShiQ4bE6+uvFzYOEREpb0oGitjmm8fr888XNg4RESlvSgaK2JZbxuuzzxY2DhERKW9KBorYsGHQpQtMmgTTphU6GhERKVdKBopYdXX9qYLnnitsLCIiUr6UDBQ5nSoQEZF8UzJQ5JQMiIhIvikZKHJbbBGv48fDwoWFjUVERMqTkoEi16MHDB0KixbByy8XOhoRESlHSgZKgE4ViIhIPikZKAFKBkREJJ+UDJSA9GTAvbCxiIhI+VEyUALWWw969YobD02eXOhoRESk3CgZKAFm9bUDt99e2FhERKT8KBkoEcceG69nnglPP13YWEREpLwoGSgR++wDp5wCS5bAgQfCZ58VOiIRESkXSgZKyAUXwHbbRduBgw+GxYsLHZGIiJQDJQMlpKYm2gysvjo89RRceWWhIxIRkXKgZKDE9OkD11wT7//8Z1iwoLDxiIhI6VMyUIL23BM23jhOF/zjH4WORkRESp2SgRJkBqefHu8vvDCeWyAiIpIrJQMl6vvfh8GDYcoUGDWq0NGIiEgpUzJQoqqq4LTT4v3558MNN8APfgDDhsHddxc0NBERKTFKBkrYIYfA2mvD++/D0UfDbbfBW2/FfQj++tdCRyciIqVCyUAJq6mBSy+Fvn1hjz3g8svhrLPiYUa/+EXUHOjBRiIi0pyaQgcgK2affaJLt9Zacfvi88+HsWPjZkXbbluQ8EREpASoZqAMHXUU/PvfsPLK8NxzcdfCPfeMSxFFREQaUjJQpnbfHT74AM45B7p0gQcfjFMHIiIiDSkZKGNdu8LZZ8Mbb0BtLdxzTzQ2FBERSadkoAIMGABHHAF1dXDxxYWORkREio2SgQpx6qnxev318PnnhY1FRESKi5KBCjF4MHzve7BwoZ52KCIiy1IyUEF+/et4veoqmDOnsLGIiEjxUDJQQbbcErbeGmbNgr32itsW6yFHIiKiZKDCnH8+dOwITzwBBxwA/frFjYlERKRyKRmoMFtvDR9/DJddBkOHwhdfwEkn6bbFIiKVrE2SATMbaWYTzOwzMzszi/F/b2azzGyemV1jZtXJ8G5mdq+ZzTCzx8xslfxHX3569YITT4SXXoLeveG11+CFFwodlYiIFErekwEz6wncC5wO9Ae2N7Pdmhj/AGBvYFNgILAzcHDy8ZVAHbAacBtwRf4iL3/t28fTDgH+9rfCxiIiIoXTFjUDewCT3P0ed19M7NAPbWL8z4EfuPtEd/8YeAYYYGa1wAHAOcl8rge2M7OOeY6/rP34x/F6++0wc2ZhYxERkcJoi2SgP/BSWv8k4og/I3d/yt3fBTCzDsCOwFNAb6AaeCsZrw6YBgzIS9QVYp11YJddYMECuOmmQkcjIiKFYJ7nlmNmdjrQ3d1/nfSvC/zH3RtNCNKmPRf4rrvvbGZ9gVfcfZW0z58GfuXuz2WY9njgeIA+ffqMuO2221aoHHPmzKFLly4rNI9i9fTTvTnzzGH06zePG298EbMYXs5lzqTSyguVV+ZKKy+ozJUgU3m33377l9x9k6xn4u557YCfANem9W8IvJ3FdNsCXwD9k/6OwHySBCYZ9jKwWXPzGjFihK+oMWPGrPA8itXixe6rr+4O7qNH1w8v5zJnUmnlda+8Mldaed1V5kqQqbzAeG/BvrotThM8k+zYUzYHPmpqAjMbANwOHOnuUwDcfT7wdjI9ZtYVGAxMafWIK0xNDRx3XLy/5JLCxiIiIm0v78mAu78BzDSzi8xsR+AM4JbGxjez7sBjwCXu/lCDj68FrjazbYCrgWfcfVqeQq8oP/sZdOoEDzwA48YVOhoREWlLbXXTof2IywEvBq5291FmdqqZXZ5h3COBdYFTzGxa0l0O4O7XAKOAy4F2wBFtE375W2UV+MUv4v1ZZxU2FhERaVs1bfElydH7EQ2G/aWRca+gifsHuPslgCqz8+DUU+MhRg8/DM8+W+hoRESkreh2xPKt3r3j1sSg2gERkUqiZECWcfLJ0L07PP44vPpq90KHIyIibUDJgCyjZ89ICACuvnpdliwpbDwiIpJ/SgZkOSefDGuuCRMnduXiiwsdjYiI5JuSAVlOly5w7bXx/uyz4b33ChuPiIjkl5IByWiXXWDXXaexcCEceyzU1RU6IhERyRclA9Kon/3sfVZZBZ56Cq67rtDRiIhIvigZkEZ167aEK5I7Ppx7LixcWNh4REQkP5QMSJMOOgiGDYOpU+HmmwsdjYiI5IOSAWmSGfz2t/H+ootg6dLCxiMiIq1PyYA06+CDYa21YOJEuOeeQkcjIiKtTcmANKumBn71q3h/wQXgXth4RESkdSkZkKwcdVQ82fDll+GxxwodjYiItCYlA5KVjh3hl7+M9yecAF98Udh4RESk9SgZkKydcAJstFG0Hdh1V5g1q/6zadNg0aLCxSYiIrlTMiBZ69IFHn4Y1lsPXn0V9toLLrsMNt0UVlsNdt9dVxuIiJQiJQPSIn36wH//C2usAc88E6cOxo+Pz0aPjuRARERKi5IBabH+/SMh2GQT2GcfuOMOuPPO+Oz00+Httwsbn4iItExNoQOQ0jRwIIwbt+ywY46Bf/wDfvhDeO45qK0tTGwixWT2bHjtNejWLe7X0a1boSOSYvPVVzBlCnznO4WLQcmAtJpLL4XHH4eXXor2BCNHxq2Md9wROnUqdHQi+eUOEyZEA9sPP4xHfz/3HLz++rJP/ezZE3r3jqSge/fYAeywA2y7rRKFSrFoEdx2W9SwvvBCrCtrrgmTJxcuJiUD0mq6dYPrr4/HHz/6aHQAQ4fGRrFr18LG15A7LFmiGgzJ7OuvY+deVwdVVfVddfWy/d98A/fdF6fLPvxw+fnU1MRVOPPnx8b+q6+iSxk9OhLp6upohHvKKbDddnEr8Hxwj+/8+ONoA9SnD6yzTiQmTU0zZ0597LNnR3nmz4/l06FDXH7ct280MC4nS5fGevD117B4cWwzVl8d1l0X2rVrfvqFC2O6Dh1iWd1wA/zxj1ETkNK+fcxz/vxYjoWgZEBa1fbbR5uBJ5+Et96Ce++N1x/9CG6/PbcN3NKl0VDxiy/g8MNht91iA7siZs6EAw6IqyJuvhn23LP+sw8+iDLsuWds7NvClCnxqOi334Ytt4yEqrY2HhB10UUwalTEe+GF9Rvtr7+OndA339TvmPr1g8GD4yijunrZ75g8OabfcceYV6Vzj/Xg009jOU+dGu8nTYIXX4wdQEvvtrnqqjB8eJwOWGstGDECNtusvmbMHaZPr9+hzpgBzz4bO+cXX4QHHohu+PA43bbddrDhhq2zHi5dCnffDX/6U5y2SFdTE9/1ve/FDn38+OhSycusWbETzMZmm8Gxx8a86upiB7dgQUy/dOmyXW1t7FR79Gh6ngsWxA3P3nwzkpJUIpLqli6NZd+vX7y++OLK3+7A27ePK6HatYvfePJk+OST+H907Lhs16FDLIuqqtiBv/RS/D7ffLN8TDU1EfvgwfWdGXz5ZfzG774btUITJ9bXDJnVr1NDhsBPfgLf/W78xtkkFvlkXgH3lt1kk018fKrJe47Gjh3LyJEjWyegEtEaZX733bj08Jtv4JJL6m9c1BKnnQbnn1/f36dPPE1xp52iarVHj/jTT5kSf/q11mp6fh9/HAlFqqFjVRVccQUMHPgEL7ywHeedF9n84YdHG4h81Ry4w//9H/zhD/DRR8t+1rs3bLVVXMqZ/ujo1VePHfprr8E112TeSEFs1HbaKdpx7LYb/O//wplnwrx5sUG6/no48sjCr9effRZVpCutFGVeeeUVT/Qacq9PQh999Am++WY7brwx7qS5YEHj09XWxmmu1BFdqlu6dNl+s1gPDz4Ytt56+SQsW9Onw9VXw1VXxfuUHj1gm21iZ73ddpHo1dbGzqO2NpaXWey0J06MZHbevPrPRo+exIwZa/P88/D55zHP1VaLxH3GjLhHyFtvNX9ZcKdOcYqjZ8+o5evcOXagVVWxHOfNi53f11+3vOyp2onUfNu1i/V+wYI4CHj99dg5F8qAAbHMamujvFOmRC1QNrvP6upYh1K1KMOGxX/xgANa72Aj0//YzF5y902ynYeSgSwVeqNZCK1V5rvvjhW/uhrGjIkNW2MWL66vioWoej344Og/8UT4z38iwUipqooNSPoGaPPNY0e37rpxquKRR2Kjt9FGccR1441xZDBkCOyxB/zlLzFd794LmTGjPRAbo0WL4uZKd90VRxdvvBF/6C23XPEq3A8/hOOOizYWEBv8rbaCQYPgoYfqExWzWHZHHBFHdM8/v+x8ttsuTsO4x7L78MM4op06tX6c2tr6DekWW8Q8zKJGpG/f+I3Td5i5WLo0duqvvBIbyq++iqPuHj0ixq23Xvbo77PPojzXXrvszaq6dYNDDonbX2+xRX1M8+ZFEvfxx/H7v/pqfNe0aXEk279/JEpdu0ZCuGhRjPPyy5Fode4c8549ewlz59ZnG927x/Srrx5d377RjRgR60uHDrkvk1zNnx/r3H//C088sXyimElNTXZH7mutBb/5Tfw/0ss2c2b8t+6/PxLMESPiaqHBgyNR69EjuyPXefPiYWZ//3v8Ph07RhLRvn39EXd1dX03b14kME0lZRDrwbBh8f/t0WP5I/qqqlinPv44Ep75879g0KBV6NYtkoq5c+M7Vlstkql+/eI/s2DB8rUMqSQPovxbbx3rREPz58e6OGFCdO+8E2Xq3Rt69YK1144j/kGD6pf1kiWtn+xC6yQDuHvZdyNGjPAVNWbMmBWeR6lpzTKfeqo7uPfq5f7WW5nHefFF97593Vdayf3YY91vuMG9U6eY7rLLYpy6OvfnnnM/6yz3rbd2r6mJzzt2dB80yL1r1+hvrttqK/cvv4x53nije21tDF97bffHHotYVl45hq2+esw/Ne2++7rPmpW5DF995X733e5Tp2b+fPLkWBapcvXq5X7LLe5Ll9aPU1fn/uqr7lde6f7GG/XDlyyJYWus4X7QQe7jxjW+vKdNc7/4Yvdhw+J7+vVzf+CB+Oy882JYVZX7iBFf+rrrRvm32sr9lVcan+eSJe6zZ7t//XWU88kn3c8+232bberL01hXVeW+zjruG2/svu229cvTzH2zzdyHDKlf3qmue/f4Pdu1y+43zbbbeGP3Sy91/+yzxstaTCZPjnX0mGPc118/1plu3WIZVlfXl6tzZ/fvfMd9//3djz7a/fDD3Q8+2H2//T72UaPc33132fWsWCxdGmUcO9b9wQfd77zTfdQo97vuinX2ySdjnWuJStteZyovMN5bsJ9UzUCWVDOwYpYsiXOIDz4YR2BPPRWZc8qdd8Y50kxHCEceGdXamY5c582LrL937/h83rw4j37TTXHubocdopq8X784Uhk3Lo4GTz552YY648bBqFETOf/89b49v/v++1EzMGlS9K+3XlRZfv11vL/7bthgg/p5TJwY7QwmToz+TTeFnXeOI/P58+PI+f776486DjoIrrwyHgCVL+5RC7LKKnF0lnL22XDuucuPX1UVNTBnnhnVwRDnaK++OmpQ0quvG+rfHzbeOJZNr14x/SefwNix0WK6YTXv978fMQwbVj/s7bej5uamm+KoP6W2Nn7Dfv3i6HajjeK7+vWLWpApU+LIcO7ciBfitxk+HNZfP5b/11/Dc889x4EHfrdFy7DY1dXFsm3XLvN/RNuu8qeaAdUM5FVrl3nePPeRI+MoZsAA90ceiSOA//mf+qObH/0ojk7POCOOFnfbzX3+/FYNo1GZyjtnjvvTT7vPmBH9778fR1/g3qGD+3HHxRH6k09GjQbEkXuHDpmPSmtr3Q87zP2FF9qmTI2pq3N/6in3P/zhdX/rLffPP3c/8cQ4gk/FOnBg1ED07l0/rFMn9y5d4oh9yBD3E05wv/de9+nTm/6+uXPd33vPffx499Gj3SdMaHr8JUvcv/giamDmz494W4P+x5Wh0srcGjUDuppA2kzHjnFkvPPOcaS46671n5nBn/8cR+xmceR33nmFizWlc+c4l5+yzjrRuviEE6K24rrroku1Et5zz7h+uKoq2gO88EKcI+zQIWok9t03zlsWmlmcC12y5EuGDIlhl10WtTO/+hU8/XScD021z/jud+Gcc+K3y6VtQadOLbvkrLo6GhOKSNtQMiBtqmvXaCD3059GY7c114xu772jRXYp6NQprjL49a+jAdyNN0YDrBNOiB1qqvHj3ntHV0qGD48kZuHCaDD5yivREHPkyPxd9y4ihadkQNpcz55x9FzqBg2KyyX/9KdoxVxON1tp3z5ak2+S/RlHESlhelCRyArq0KG8EgERqTxKBkRERCqckgEREZEKp2RARESkwikZEBERqXBKBkRERCqckgEREZEKp2RARESkwikZEBERqXBKBkRERCqckgEREZEKp2RARESkwikZEBERqXDm7oWOIe/MbDrw0QrOpjcwoxXCKSWVVuZKKy9UXpkrrbygMleCTOVd091XznYGFZEMtAYzG+/uFfVA10orc6WVFyqvzJVWXlCZK0FrlFenCURERCqckgEREZEKp2Qge9cWOoACqLQyV1p5ofLKXGnlBZW5EqxwedVmQEREpMKpZkBERKTCKRlohpmNNLMJZvaZmZ1Z6Hjyxcx+bGZfmNkiM7vLzLqYWa2Z/d3MppvZC2a2bqHjbG1mtnJS7gEVUt4qM3vezI5I+su2zGZ2oplNSX7fv5tZh3Isr5ltYGZvpPU3WsZy2Z5lKPNqZva4mS1MfvP90j4ryzI3+OwcM7shrb/lZXZ3dY10QE/gK2A/oBYYDexW6LjyUM7NgA+B4UAv4HngNOD3wLNAF2BH4PlCx5qHst8LODCgQsp7EjA2rb8sywwMAt4BugEdgIeAX5VbeZP/7CRgcnO/ablszxop8wPABUBX4FBgdvK7l22Z0z7bBFgM3LAiv7NqBpq2BzDJ3e9x98XAlcSKVm4WAQe6+8vu/iXwGLFzPBQ4393nuPvjQAczW7uAcbYqMzsK6A5MSQaVe3nXAs4GfpY2uFzLPAx43d1nu/sC4GlgTcqvvL8ETmwwrLEylsv2bJkym5kBdwO/c/dvgDuIpGAlyrTMKWbWAbgRuDVtcE5lVjLQtP7AS2n9k4CBBYolb9z9VXcfnzZoT+Apli//h5RJ+c2sP3AOcCRRMwBlXN7ElcBc4AIz+7OZ9aR8y/waMNLMhppZX+AHwIOUX3l/CDSsOm6sjOWyPVumzB6u9+SwmNh+TXL3qZRpmdNcQNR6jU4bllOZlQw0rQqYldY/F+hRoFjahJkdQ1Sv3UqZlj85kriBOJL4OO2jsiwvgJltSmwkbyYuQ9oA+BdlmmlZRAAAC8FJREFUWmZ3nwg8AtwG/Af4GHicMitv2g4wXWNlLIuyN1JmAMysI3AhkDpPXrZlNrPtgZHA6Q0+yqnMNSsQXyX4kqhaTOkELC1QLHlnZgOBi4Bd3X2JmX1JrETzklHKpfwnAp+5+60NhpdreQG2Al50998BmNnzwHSifGVXZjM7FFjk7hsk/ecCV1Hev3FKY2WshO3Z5cCb7v7PpL8sy2xm3YC/AQe4+8I4vvlWTmVWzUDTngG2TevfnBV/4FFRSqqM/wWc5u6pKqZvy29mVURDlXIo/0HALmY2zcymAf2AcUB7yrO8AEuAyWn9C4gNxL8pzzJvC7yZ1v8csDXlu06na6yMZb09M7OfEeU7Jm1wuZZ5W2BV4LFkG3Y5cLCZPUWOZVbNQBPc/Q0zm2lmFxFVjmewfJVMyTOzWqIq9XF3T7+T1bXANclTH3cjdiDjChBiq3L3LdP7zWwyUd02kDIsb+IR4PSkIdmHwKnERuNvlGeZJwI/MLMnAQN+A7wO/B/lWd50Gf+37l5XrtszM9sHOBfY0t1np4aX6zbc3R8gGj8D3zaGHunuRyX9LS9zoS+ZKPaOyL5uBl4FflPoePJUxn2IRnTTgWlJd0/y2RHAi/x/e2ceo1V5xeHnJwwUhrpWLeICVesCiEBEjEtViFCl2tSqWNq6RDGN1mhtjbRuVUxs65rY2qhRjKWuNURrtYqAihaXoMZiiQJSgtqKCoKVfX7947yfXD5mdKptJzNznuRL5rv3vfe+753kO+c9aygLe7T1XP9H618I9O3o6wWOJXbL7xKKQL+OumagB3BbWetqIl6gT0dcL5H5s7DuWLNr7Ci/Z/VrBmYDKyu/X/8gFIMOu+a6cydTUgs/65qzHHGSJEmSdHIyZiBJkiRJOjmpDCRJkiRJJyeVgSRJkiTp5KQykCRJkiSdnFQGkiRJkqSTk8pAkiRJknRyUhlIkjZE0q2lYEhrxu4raXjluyQ9IumQT7ru8yJpF0kTWjl2dKkPX/u+uaSRqquX+jnm0q0VY7qUbm617+Mk7SzpfEm/kLSPpLGSGiW9KKnxvzG3JGnPZAXCJGlb1hC9yAGQNIUoH7sM6AfsZ/vVcroXMEnSn4GzicIyo4CdJFULhtxq+5rKPe8Ffmn7s1bauw44SNIDtue0NEhST6Kk9U5EARiIngg3ENUd19WNHwPcTVRE3BKYYvssSW8CHxKVAxtt9ynjtwSelLSWKJK1LdGv/S2gP1F1cA2xyekmaYjtNeU+txGll9cRnSrnlGu62/5XC+v5NTCCKGBTz47AnbYvauZckrQ7UhlIkralqXxqNADn2r5X0kxCuAFge6akwURvhWFE+dUdgeOAtwlBvJhoaQqApEGEcvFK+d4IrABqCgaE0jHa9lP1k5P0I6I3/AjgHklH2V7Ywlr2Ambafqdy7NvAo8C+xTiw1vbL5dwaokzuocU6Mqgc717m3AhMrax/maSxRL92gOHA5uX+5xPCfmk5d3dRBABeJHrbb1XGN5X3cRDRqrslVn3Cudr8k6RDkMpAkrQBkoYA5xJCb4CkkcBpwABgfhlm4AuSutpeB2B7haS5RPnVI2y/KelE4FlgCnCs7b9VHjWeqEu+c+lK+Ueim9+AylxmULFOVI6fRpQ5PdT2+5LOBqZKOsn203VjpxPKwCpJiwlBOQj4VpnbRGLHPhDoXS5rArqWHX9PYL2k3YAuwD8JJaWxuv5y/UjgUsIysA0wEzgLeIFQin4DPEO0LYboR9AVGEpYId4gFKkmYKikA8u4BtvVvu9dgCdoXmEYVc4nSYcglYEkaRsWA7cTpv+/AtOBC4hd+0tlzDTgXmA50XkMSYcTJu/BtudKGkEI2f2A79u+ufYASf0JC8IPgauAlbYf/DT3vaRewOXAwcBI2+8D2H687ODvkfQwcG3FbbAWGGt7hqS+RIOUnwL32T693PeLbNxJsAuh/Ewhaqn/Cbiz3LdJ0tvAAmCBpIttTyJ+s4YDtxA7/YbyvTdwZZlHv3K8xgjgD4SC8DrRg2Mi8A6wu+1FkuYAh5d5NhCuhTuA3Vt4TQ8Br0rqTlg7mloYlyTtglQGkqQNKKb0qZK+CcyxPU3StsDJNcFi+xLgkto1koYRAupIYHtJ5xE7/2MIwfaopNMJofp7YB9i9zyDUCiOLrfqJqkqlPtVntGfEOTTiWZGzxcffY1uRBe0ocA0SQPLWuqFocvn9uK7n12uXV0ZsxnwUsVNMAD4ObBQ0p5lzEnActtvlXcyGZjc8pvdGEm7Ag8QcQ+NwPbADkS3xm7l70XAl9gQGzCqrHEVEbvQEj3LPY+nY7TFTTox2agoSdqIEmE/CdiVMKvPJnbxiwlB2pXYMW9t+yNJDxFBa78rwW1dicDA+eV+mwNnEjEERxOBdb2Am4GzbC8p41bZrkbbzwAusD2rfB9ue1bZIa+v7nrLsSbb6yX1tP1ROf4IYcJfWea1yvaeksYBZ9veX1Jv4FHbA8s1Y4Af1ykDTYSC80J55GDgetuXlWv2Jkz3S9lUAYGwNmwNfMX2B+WavoTb4Sng67aXlEDNDwgrzK3ALNt71f1/riGE/dvl0GjgkfL3LsAC25c3M4ckaXekZSBJ2gBJ3yVM912Au4DLipDaCbjB9lNlzJiawCVaEE+QtIzINgAYVTH7dydanA4pz9icMMuvIawGH9o+mLAMzKtMp091bkUROINQTGjOrSDpmJoSUmFcxU1QE5p3AldIOpTYgVeD8hqBYZIWUjIlgIuIoMMxwHpCobmjPLMBeM32tptMaNP5dZXUlWj7OpV4z43AY5K2AK4gWvxeSwRTPtvMbb5MWFfeLd+HVObfG2g2CyFJ2iOpDCRJ2zADOIRIEXy6tmsnMgLGS5pN+NxPqF1ge5WkVcB1ti8tqXznA9fYXq6oQfCLyvjlkq4mIueft72inFpje7fauGIZqGcLItXvwvoTRXi3qkZJ8f3XlJ4ebGx23w64y/apNcuA7dWS7inrWgo8ZvuNMv544BxJ6+sesz+bCvMG4LclhqKvpGeAM4C5RFrhwyX4cgvCFXNtM9PvDrwM1AIyh7Ihu2Foa99BkrQHUhlIkjbA9mJodtd9NzCBCB683/YrdeerEexDiGj/yyrHqoFz2L6+PKdR0mDbL7Zyip8WENdq/6LtG8ocDiBM8zX6s0HQVplIWDQagAMq95kMTFYUNToP+FVRHkykCW4GnEi4Uj5O+yuxGPOAm8q63gTeL6fvIoIlP07HrHAVYU2osRaYVf5+rnKPJGn3pDKQJG1LvTYwsBzbG5glaWfbiyrn/wJY0nbA1YTgPF3SKYQicc7HN5b2I/zvg4E9CYF33H8wr/ElwLGeHZo5thkhqGsxA83l4A8C3itz6wIcVZlPLf+fcrwbYZK/UNKVtheU60YRQvolIu2w+pw+hHJ0uaRLgUm2m4r75QeEC2IIYSm5QNKrwKlEuuX9ko6rxBn0AK4nYiBqik8vIrCw9n6WAs29nyRpd6SZK0naCEmTCGG4TNI3JD1BuAkuJATuMmC2pLckHVncAH2A7xBm8Rtt30Kk2U0EDiMyFH5WHrGc8GufSwTVnSNpCbBO0rzah/CLT5H0k8r0GoCbbA+o/xB+/I0sEIR/f2xxPxzIhhx/JJ0paT5wMRHMCJHutwJ4VtJNhHtjmaRphNn+cCJdsgfwuqRjFSWGTwAm2P4eoTA8B/zd9jrbb9g+jHAHDCOUpgZJN5Zxi4ggwIsIi8N44AhgLGEteFxSt5JaOb28/5WEUlLLLKj9vRLoKalmKUiSdk1mEyRJGyHpaGBpCRbckYghuK/OxN0V+BrwJDCO8FU/CTxoe5MKeYoKhVvZnvb/WENrKGb6LYF5rvzgSOpl+0NJuxDCdQlwCjDZ9urKuP7AXNv1sQJIGg08Y3v5Jzx/D2B+pXARkr5q+7W6cdvYfu8zLzRJ2jGpDCRJkiRJJyfdBEmSJEnSyUllIEmSJEk6OakMJEmSJEknJ5WBJEmSJOnkpDKQJEmSJJ2cfwNFKnBLJcKELAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制图像\n",
    "plt.figure(figsize = (8,6))\n",
    "plt.plot(N_list, vol_port, 'b-', lw = 2)\n",
    "plt.xlabel('投资组合的股票数量', fontsize = 13)\n",
    "plt.ylabel('投资组合波动率', fontsize = 13)\n",
    "plt.xticks(fontsize = 13)\n",
    "plt.yticks(fontsize = 13)\n",
    "plt.title('投资组合中股票数量与投资组合的波动率之间的关系', fontsize = 13)\n",
    "plt.grid('True')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 资本资产定价模型"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据导入及预处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 导入外部数据：'data/沪深300指数（2017-2019年）.xlsx'\n",
    "data_index = pd.read_excel('data/沪深300指数（2017-2019年）.xlsx',\\\n",
    "                           sheet_name = 'Sheet1',header = 0, index_col = 0, parse_dates=True)  # 导入外部数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 计算沪深 300 指数的日收益率的时间序列\n",
    "R_index = np.log(data_index/data_index.shift(1))  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 缺失数据处理\n",
    "R_index = R_index.dropna()  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>沪深300</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>730.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.000279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.011285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-0.060192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-0.005158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.005652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.057775</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            沪深300\n",
       "count  730.000000\n",
       "mean     0.000279\n",
       "std      0.011285\n",
       "min     -0.060192\n",
       "25%     -0.005158\n",
       "50%      0.000350\n",
       "75%      0.005652\n",
       "max      0.057775"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "R_index.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 资本资产定价模型：线性回归"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 导入 statsmodels  的子模块 api\n",
    "import statsmodels.api as sm  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 对自变量的样本值增加一列常数项\n",
    "R_index_addcons = sm.add_constant(R_index)  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 重庆啤酒"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 获得索引\n",
    "index_cqpj = R.columns.get_loc('重庆啤酒')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "index_cqpj"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>          <td>重庆啤酒</td>       <th>  R-squared:         </th> <td>   0.190</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.189</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   170.8</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Thu, 26 Nov 2020</td> <th>  Prob (F-statistic):</th> <td>3.18e-35</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>14:39:40</td>     <th>  Log-Likelihood:    </th> <td>  1766.5</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   730</td>      <th>  AIC:               </th> <td>  -3529.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   728</td>      <th>  BIC:               </th> <td>  -3520.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "    <td></td>       <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th> <td>    0.0012</td> <td>    0.001</td> <td>    1.469</td> <td> 0.142</td> <td>   -0.000</td> <td>    0.003</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>沪深300</th> <td>    0.9244</td> <td>    0.071</td> <td>   13.071</td> <td> 0.000</td> <td>    0.786</td> <td>    1.063</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>36.560</td> <th>  Durbin-Watson:     </th> <td>   2.104</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td> <th>  Jarque-Bera (JB):  </th> <td>  63.498</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.362</td> <th>  Prob(JB):          </th> <td>1.63e-14</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 4.250</td> <th>  Cond. No.          </th> <td>    88.7</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                   重庆啤酒   R-squared:                       0.190\n",
       "Model:                            OLS   Adj. R-squared:                  0.189\n",
       "Method:                 Least Squares   F-statistic:                     170.8\n",
       "Date:                Thu, 26 Nov 2020   Prob (F-statistic):           3.18e-35\n",
       "Time:                        14:39:40   Log-Likelihood:                 1766.5\n",
       "No. Observations:                 730   AIC:                            -3529.\n",
       "Df Residuals:                     728   BIC:                            -3520.\n",
       "Df Model:                           1                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const          0.0012      0.001      1.469      0.142      -0.000       0.003\n",
       "沪深300          0.9244      0.071     13.071      0.000       0.786       1.063\n",
       "==============================================================================\n",
       "Omnibus:                       36.560   Durbin-Watson:                   2.104\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               63.498\n",
       "Skew:                           0.362   Prob(JB):                     1.63e-14\n",
       "Kurtosis:                       4.250   Cond. No.                         88.7\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 构建计算重庆啤酒股票的普通最小二乘法的线性回归模型\n",
    "model_cqpj = sm.OLS(endog = R.iloc[:,index_cqpj], exog = R_index_addcons)  \n",
    "# 拟合线性回归模型\n",
    "result_cqpj = model_cqpj.fit()  \n",
    "# 显示回归结果\n",
    "result_cqpj.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "const    0.001172\n",
       "沪深300    0.924378\n",
       "dtype: float64"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_cqpj.params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "重庆啤酒的资本资产定价模型表达式为：E(R) = 0.001172 + 0.924378×Rm \n"
     ]
    }
   ],
   "source": [
    "print('{}的资本资产定价模型表达式为：E(R) = {:6f} + {:6f}×Rm '.\\\n",
    "      format('重庆啤酒',result_cqpj.params[0],result_cqpj.params[1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 贵州茅台"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 获得索引\n",
    "index_gzmt = R.columns.get_loc('贵州茅台')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "index_gzmt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>          <td>贵州茅台</td>       <th>  R-squared:         </th> <td>   0.408</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.407</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   501.2</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Thu, 26 Nov 2020</td> <th>  Prob (F-statistic):</th> <td>7.11e-85</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>14:39:40</td>     <th>  Log-Likelihood:    </th> <td>  2023.9</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   730</td>      <th>  AIC:               </th> <td>  -4044.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   728</td>      <th>  BIC:               </th> <td>  -4035.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "    <td></td>       <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th> <td>    0.0014</td> <td>    0.001</td> <td>    2.532</td> <td> 0.012</td> <td>    0.000</td> <td>    0.003</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>沪深300</th> <td>    1.1129</td> <td>    0.050</td> <td>   22.388</td> <td> 0.000</td> <td>    1.015</td> <td>    1.210</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>45.671</td> <th>  Durbin-Watson:     </th> <td>   1.958</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td> <th>  Jarque-Bera (JB):  </th> <td> 168.899</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.107</td> <th>  Prob(JB):          </th> <td>2.11e-37</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 5.347</td> <th>  Cond. No.          </th> <td>    88.7</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                   贵州茅台   R-squared:                       0.408\n",
       "Model:                            OLS   Adj. R-squared:                  0.407\n",
       "Method:                 Least Squares   F-statistic:                     501.2\n",
       "Date:                Thu, 26 Nov 2020   Prob (F-statistic):           7.11e-85\n",
       "Time:                        14:39:40   Log-Likelihood:                 2023.9\n",
       "No. Observations:                 730   AIC:                            -4044.\n",
       "Df Residuals:                     728   BIC:                            -4035.\n",
       "Df Model:                           1                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const          0.0014      0.001      2.532      0.012       0.000       0.003\n",
       "沪深300          1.1129      0.050     22.388      0.000       1.015       1.210\n",
       "==============================================================================\n",
       "Omnibus:                       45.671   Durbin-Watson:                   1.958\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              168.899\n",
       "Skew:                           0.107   Prob(JB):                     2.11e-37\n",
       "Kurtosis:                       5.347   Cond. No.                         88.7\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 构建计算贵州莫茅台股票的普通最小二乘法的线性回归模型\n",
    "model_gzmt = sm.OLS(endog = R.iloc[:,index_gzmt], exog = R_index_addcons)  \n",
    "# 拟合线性回归模型\n",
    "result_gzmt = model_gzmt.fit() \n",
    "# 显示回归结果\n",
    "result_gzmt.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "const    0.001420\n",
       "沪深300    1.112859\n",
       "dtype: float64"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_gzmt.params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "贵州茅台的资本资产定价模型表达式为：E(R) = 0.001420 + 1.112859×Rm \n"
     ]
    }
   ],
   "source": [
    "print('{}的资本资产定价模型表达式为：E(R) = {:6f} + {:6f}×Rm '.\\\n",
    "      format('贵州茅台',result_gzmt.params[0],result_gzmt.params[1]))"
   ]
  }
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